Top 12+ AI Programming Languages to Learn in 2025

Why should you care about AI programming languages? Picture this: you're building a recommendation system for an online store. You want it not just to suggest products based on user preferences but to learn from those preferences over time and get smarter with each interaction. To make that happen, you need to choose the right programming language.

Just like picking the right tool for a DIY project, choosing the right AI programming languages can make or break your system's performance, scalability, and efficiency.

If you’re serious about creating powerful AI applications, the language you use is crucial. Ready to take your coding skills to the next level? Let’s dig into the AI programming languages that can help you bring your ideas to life!

12+ AI Programming Languages For Beginners to Consider for Your Projects in 2025

When diving into AI, it’s important to recognize that different programming languages excel in different aspects of data science. Some languages are tailored for statistical analysis, while others are optimized for machine learning, big data processing, or data visualization.

Here’s a breakdown of 12+ AI programming languages and their strengths:

Also Read: How to Build Your Own AI System: Step-by-Step Guide

Each of these languages has its unique strengths, making them ideal for different AI-related tasks.

Let’s get into the details:

1. Python

Python is one of the most popular and versatile AI programming languages for beginners. It is the best programming language for AI and machine learning.

Basic Applications

• Data science and analytics.
• AI, machine learning, and deep learning.
• Mobile web development, automation, and scripting.

Characteristics

• Python code is executed line by line, making debugging easier.
• Abstracts low-level operations, focusing on ease of use.
• No need to declare variable types, reducing complexity in coding.

Advantages

• Simple syntax ideal for beginners.
• A robust ecosystem of AI libraries and frameworks.
• Active community support and frequent updates.

Disadvantages

• Compared to compiled languages, Python can be slower.
• Python can consume a lot of memory for large-scale tasks.
• Limited support for mobile app development.

Also Read: Top Python Libraries for Machine Learning for Efficient Model Development

Kickstart your AI journey with upGrad’s free Basic Python Programming course and build a strong foundation in coding for data science and AI!

2. Java

Java is one of the widely-used, high-level AI programming languages for beginners known for its platform independence and object-oriented features.

Basic Applications

• Web development.
• Mobile applications (Android).
• Enterprise-level applications.

Characteristics

• Code is compiled into bytecode, making it platform-independent.
• Strongly typed language, requiring explicit declaration of variable types.
• Robust memory management through garbage collection.

Advantages

• Highly scalable and reliable for large applications.
• Extensive libraries and frameworks (e.g., Spring, Hibernate).
• Strong community support and widespread use in industry.

Disadvantages

• Can be slower than natively compiled languages due to JVM overhead.
• Verbose syntax compared to some modern languages.
• Memory management can still be complex, despite garbage collection.

Also Read: 45+ Best Java Project Ideas for Beginners in 2025 with Source Code & Best Practices

Get started with Java by enrolling in upGrad’s free JavaScript Basics course and master the fundamentals to build dynamic web applications!

3. C++

C++ is a powerful, high-performance programming language commonly used for system software, game development, and applications requiring real-time processing.

Basic Applications

• System software (e.g., operating systems).
• Game development.
• Real-time simulations and embedded systems.

Characteristics

• Supports both procedural and object-oriented programming.
• Provides direct memory manipulation through pointers.
• Requires manual memory management (allocating and deallocating memory).

Advantages

• High performance, making it ideal for resource-intensive applications.
• Gives developers more control over system resources (e.g., memory, CPU).
• Rich set of libraries and tools, especially in areas like game development and systems programming.

Disadvantages

• Complex syntax, which can make it harder for beginners.
• Manual memory management can lead to errors.
• Longer development time compared to higher-level languages due to its complexity.

Also Read: Top 25 C++ Project Ideas For Beginners

4. R

R is a programming language and environment specifically designed for statistical computing and data analysis.

Basic Applications

• Data analysis and visualization.
• Statistical modeling and machine learning.
• Bioinformatics and computational research.

Characteristics

• Rich set of packages and libraries tailored to statistics and data analysis.
• Extensive support for data manipulation, visualization, and statistical modeling.
• Interactive environment that allows real-time data exploration.

Advantages

• Excellent support for statistical analysis with built-in functions and packages.
• Highly extensible with a vast ecosystem of contributed packages.
• Strong visualization capabilities, ideal for creating complex data plots and charts.

Disadvantages

• Slower performance compared to compiled languages.
• Memory management can be inefficient for very large datasets.
• Less versatile for general-purpose programming or applications outside of statistical analysis.

Also Read: 10 Interesting R Project Ideas For Beginners [2025]

5. Julia

Julia is a high-level, high-performance programming language designed for numerical and scientific computing, with a focus on ease of use and speed.

Basic Applications

• Scientific computing and numerical analysis.
• Machine learning and data science.
• High-performance computing and simulations.

Characteristics

• Just-in-time (JIT) compilation for high execution speed, close to C/C++ performance.
• Supports multiple dispatch, allowing functions to be defined for different argument types.
• Designed for parallel and distributed computing with built-in tools for concurrency.

Advantages

• Exceptional performance, particularly for numerical and scientific tasks.
• Easy syntax similar to Python, making it accessible for non-programmers.
• Strong support for mathematical and statistical operations, with extensive libraries (e.g., DataFrames.jl, Flux.jl).

Disadvantages

• Smaller ecosystem and community compared to more established languages like Python and R.
• Can be more challenging to integrate with other languages or existing systems.
• Limited support for some traditional software development tasks (e.g., mobile or web development).

6. Lisp

Lisp is one of the oldest high-level programming languages, known for its symbolic expression and functional programming features. It is often called the best programming language for AI and machine learning.

Basic Applications

• Artificial Intelligence (AI) and machine learning.
• Symbolic computation and logic-based systems.
• Research and prototyping.

Characteristics

• Code and data are represented as symbolic expressions (S-expressions), enabling powerful metaprogramming.
• Highly flexible, supports functional, procedural, and object-oriented paradigms.
• Extensive use of recursion and support for garbage collection.

Advantages

• Extremely powerful macro system that allows for code generation and modification.
• Great for AI research due to its flexibility and support for symbolic computation.
• Interactive development environment with fast prototyping.

Disadvantages

• Syntax can be difficult for beginners due to heavy use of parentheses.
• Smaller community and ecosystem compared to more mainstream languages.
• Performance can be an issue for certain applications compared to lower-level languages like C++.

7. JavaScript

JavaScript is a dynamic, high-level programming language primarily used for web development to create interactive and dynamic user interfaces.

Basic Applications

• Web development (front-end and back-end).
• Server-side scripting (with Node.js).
• Mobile applications (via frameworks like React Native).

Characteristics

• Interpreted language that runs in web browsers, enabling dynamic content.
• Event-driven and asynchronous, particularly with features like promises and async/await.
• Supports both object-oriented and functional programming paradigms.

Advantages

• Essential for front-end web development, providing interactivity and dynamic content (HTML, CSS, and JavaScript together form the foundation of web apps).
• Large ecosystem with a vast number of libraries and frameworks (e.g., React, Angular, Vue).
• Runs directly in web browsers, making it easy to deploy applications on the web.

Disadvantages

• Can be difficult to debug, especially in larger applications.
• Inconsistent behavior across different browsers (though modern frameworks help mitigate this).
• Performance can be slower than compiled languages, especially for complex computations.

Also Read: 40 Must-Try JavaScript Project Ideas for Developers of All Levels

8. MATLAB

MATLAB is a high-level programming language and environment primarily used for numerical computing, data analysis, and algorithm development.

Basic Applications

• Numerical computing and matrix manipulation.
• Data analysis and visualization.
• Simulation and model-based design in engineering and scientific research.

Characteristics

• Built-in support for matrix operations and numerical analysis.
• Rich set of toolboxes for specialized applications (e.g., signal processing, image processing, control systems).
• Interactive environment for data exploration, algorithm development, and visualization.

Advantages

• Extremely powerful for numerical computation, especially for matrix-heavy tasks.
• Extensive libraries and toolboxes for various scientific and engineering domains.
• Easy to use with a simple syntax and high-level commands for complex operations.

Disadvantages

• Commercial software with licensing costs, making it less accessible than open-source alternatives.
• Can be slower than lower-level languages like C or C++ for some tasks.
• Limited support for general-purpose programming, web development, and large-scale software projects.

Also Read: 15 Interesting MATLAB Project Ideas & Topics For Beginners

9. Mojo

Mojo is a programming language designed for high-performance computing, machine learning, and data science, with a focus on combining the flexibility of Python with the speed of languages like C++.

Basic Applications

• Machine learning and deep learning.
• Data science and scientific computing.
• High-performance computing (HPC) and large-scale data processing.

Characteristics

• Combines Python-like syntax with the performance of compiled languages, offering both flexibility and speed.
• Designed to optimize computational performance with minimal boilerplate.
• Leverages features from modern languages such as multiple dispatch and high-performance parallel execution.

Advantages

• Extremely fast, offering C++-level performance for numerical and machine learning tasks.
• Seamless integration with Python, allowing users to access the rich Python ecosystem while achieving higher performance.
• Simplifies high-performance programming with a clean, Pythonic syntax.

Disadvantages

• Relatively new, with a smaller ecosystem and community compared to more established languages.
• Limited documentation and resources as it is still evolving.
• May require a learning curve for users unfamiliar with high-performance computing principles.

10. Go

Go (also known as Golang) is a statically typed, compiled programming language created by Google, designed for simplicity, performance, and efficient concurrency.

Basic Applications

• Web development and APIs.
• Cloud services and distributed systems.
• Command-line tools and networking applications.

Characteristics

• Statically typed with strong support for concurrency through goroutines and channels.
• Simple syntax with minimalistic design, avoiding features like inheritance and generics (though generics have been introduced in recent versions).
• Compiled to machine code, making it highly efficient and fast.

Advantages

• Fast performance, making it ideal for large-scale, performance-critical applications.
• Built-in concurrency model (goroutines) makes it easier to write scalable and efficient multi-threaded applications.
• Strong support for cloud-native development, widely used in microservices architecture.

Disadvantages

• Limited support for more complex programming paradigms (e.g., object-oriented programming, though interfaces are supported).
• Relatively young ecosystem compared to languages like Java or Python, though it is growing rapidly.
• Lack of certain features (e.g., generics until recently) can make some tasks more verbose or challenging.

11. Rust

Rust is a systems programming language focused on safety, performance, and concurrency, offering memory safety without a garbage collector.

Basic Applications

• Systems programming (e.g., operating systems, device drivers).
• Game development.
• Web assembly and embedded systems.

Characteristics

• Strong emphasis on memory safety through ownership and borrowing, preventing common bugs like null pointer dereferencing and memory leaks.
• Compiled language that produces highly optimized, fast executables.
• Concurrency support with a unique approach to prevent race conditions and data races at compile time.

Advantages

• Exceptional performance, comparable to C and C++.
• Memory safety features without the need for a garbage collector, making it ideal for low-level system development.
• Growing ecosystem and active community, with strong support for tools like Cargo (package manager) and Rust’s extensive standard library.

Disadvantages

• Steeper learning curve, especially due to its unique ownership and borrowing model.
• Slower compilation times compared to some other languages.
• Smaller ecosystem compared to languages like Python or JavaScript, although it's rapidly growing.

12. Prolog

Prolog is a declarative programming language primarily used for logic programming, artificial intelligence (AI), and computational linguistics.

Basic Applications

• Artificial intelligence and expert systems.
• Natural language processing.
• Knowledge representation and reasoning.

Characteristics

• Based on formal logic, where programs are defined as a set of facts and rules.
• Uses a declarative paradigm, where you specify "what" you want to achieve, not "how" to achieve it.
• Employs backtracking as a primary mechanism for searching for solutions to problems.

Advantages

• Well-suited for tasks involving complex problem-solving, logic, and symbolic computation.
• Highly effective for AI applications, especially in areas like pattern matching, reasoning, and theorem proving.
• Declarative nature makes it easy to express problems that involve relationships and constraints.

Disadvantages

• Less efficient than imperative languages for certain types of applications (e.g., general-purpose programming or performance-critical tasks).
• Steep learning curve, especially for developers used to procedural or object-oriented programming paradigms.
• Smaller community and ecosystem, leading to limited support and fewer modern libraries compared to other languages.

13. Haskell

Haskell is a statically typed, purely functional programming language known for its strong emphasis on immutability, laziness, and mathematical precision.

Basic Applications

• Functional programming and algorithm development.
• Compiler construction and language design.
• Financial modeling and data analysis.

Characteristics

• Purely functional, meaning functions have no side effects and computations are based on mathematical functions.
• Strong static typing system with type inference, making code more predictable and less error-prone.
• Lazy evaluation, where expressions are only evaluated when needed, improving performance in some cases.

Advantages

• High reliability and correctness due to strong typing and pure functional design.
• Concise and expressive syntax that can result in elegant, high-level abstractions.
• Supports concurrency and parallelism with a clean, declarative approach.

Disadvantages

• Steep learning curve, especially for developers new to functional programming.
• Slower execution times compared to languages like C or C++ for some performance-critical tasks.
• Smaller community and fewer mainstream libraries, which can limit adoption and support for certain applications.

14. Scala

Scala is a high-level programming language that combines functional programming and object-oriented programming, designed to be concise, expressive, and scalable.

Basic Applications

• Web development (e.g., with frameworks like Play).
• Big data processing (e.g., with Apache Spark).
• Distributed systems and backend development.

Characteristics

• Supports both functional programming (e.g., immutability, higher-order functions) and object-oriented programming (e.g., classes, inheritance).
• Statically typed with type inference, offering both safety and flexibility.
• Runs on the Java Virtual Machine (JVM), allowing seamless integration with Java libraries and ecosystems.

Advantages

• Concise and expressive syntax that reduces boilerplate code.
• Combines the benefits of functional and object-oriented paradigms, making it flexible and powerful.
• Great for working with large-scale data and distributed systems, particularly with libraries like Apache Spark.

Disadvantages

• Steeper learning curve due to its combination of paradigms and complex features.
• Compilation times can be slower compared to simpler languages, especially with large codebases.
• Less popular than Java, meaning a smaller job market and fewer resources compared to more mainstream languages.

Also Read: Build your Career with Currying in Scala

Now, let’s take a look at how key AI programming languages compare to traditional languages in terms of features, use cases, and performance.

Comparison of Key AI Programming Languages with Traditional Languages

Several important factors need to be considered when choosing the best language for an AI project. They directly influence the development speed, effectiveness, and long-term maintenance of AI systems.

Here is a table of comparison:

This comparison helps highlight the differences in how AI-focused programming languages stack up against more traditional languages, enabling you to make a more informed choice based on your project’s needs.

Also Read: Top 25 Artificial Intelligence Project Ideas & Topics for Beginners [2025]

While many languages are suitable for AI, some are not well-suited to the unique demands of AI development—let’s explore those you may want to avoid.

Which Programming Languages Should You Avoid for AI?

When building AI applications, efficiency, scalability, and robust library support are critical. Some languages are less suited for AI due to limited tools, performance constraints, or lack of modern developer support. Avoiding these can save time and effort.

Languages with Limited AI Library Support

Some languages lack essential libraries and frameworks for AI, making development inefficient. Here are a few examples:

• Ruby: Limited AI and machine learning tools.
• PHP: Primarily web-focused, lacking AI-specific libraries.
• Perl: Not widely used in modern AI development.

Languages with High Learning Curves

AI projects require quick iteration, which can be hindered by complex languages. Here are a few examples:

• Assembly: Low-level language, highly specialized, and time-consuming.
• Objective-C: Steep learning curve and limited AI ecosystem.

Outdated or Deprecated Languages

Languages no longer widely supported or used pose challenges in finding resources and skilled developers. Here are a few examples:

• Fortran: Obsolete for modern AI applications.

By focusing on modern, well-supported languages, you can optimize your AI development process.

Also Read: Top 20 Programming Languages of the Future

Choosing the right programming language for your AI project can be tricky, but understanding your project's requirements will help guide your decision.

How Do You Select the Best AI Programming Language for Your Project?

Choosing the right programming language for your AI project is crucial and depends on factors like project goals, team expertise, and application scope.

Here are the key considerations:

Project Requirements

Evaluate the specific needs of your AI project. These can be:

• Type of AI tasks (e.g., NLP, computer vision, predictive modeling).
• Required libraries or frameworks (e.g., TensorFlow, PyTorch, or Scikit-learn).
• Scalability and performance expectations.

Development Speed

Opt for languages that enable faster prototyping and iteration. They are:

• Python: Popular for its vast libraries and simplicity.
• JavaScript: Suitable for web-based AI applications.

Learning Curve

Consider your team’s expertise and the ease of adopting a new language. Here a couple of things you need to keep in time:

• Use languages your team is familiar with to reduce ramp-up time.
• Avoid steep learning curves that may slow development.

Platform Requirements

Match the language to the platform where your AI application will run. Here are a few considerations:

• Mobile AI: Swift for iOS or Kotlin for Android.
• Web-based AI: JavaScript or frameworks like Node.js.
• High-performance systems: C++ or Java.

By aligning your language choice with these factors, you can maximize efficiency and ensure your project’s success.

As AI continues to advance, it’s essential to consider how it will shape the future of programming jobs and the broader software development industry.

How Will AI Affect Coding Jobs and Change the Programming Industry?

AI is revolutionizing the programming industry by enhancing efficiency, automating repetitive tasks, and enabling developers to focus on higher-value activities. While it won’t replace coding jobs, it will transform their nature, emphasizing collaboration and creativity over manual coding.

Impact of AI on Coding Jobs

AI is reshaping coding roles by automating routine tasks and opening new opportunities. Here are a few examples:

• Automation of Repetitive Coding Tasks: AI tools handle boilerplate code, freeing developers for strategic work.
• Shifting Focus to Complex Problem Solving: Developers can focus on innovative solutions and architecture design.
• New Roles for Programmers in AI Supervision: Overseeing AI systems and refining their outputs will become crucial.
• Enhanced Collaboration Between Developers and Stakeholders: AI improves communication by automating documentation and insights.
• Increased Productivity and Efficiency: Streamlined workflows allow faster project completion.

How AI-Assisted Programming is Changing the Industry

AI-powered tools are transforming the programming landscape, enhancing development processes. Let’s go through the examples below:

• Accelerating Software Development Cycles: Automated suggestions and faster coding iterations speed up timelines.
• AI-Powered Code Generation and Recommendations: Tools like GitHub Copilot and ChatGPT suggest efficient code snippets.
• Enhanced Bug Fixing and Optimization: AI detects and resolves bugs with precision, improving code quality.
• Streamlining Testing and Deployment Processes: AI simplifies quality assurance, enabling smoother deployment pipelines.

AI is not a replacement but an augmentation, empowering developers to achieve more while redefining industry standards.

Also Read: Top 10 Highest Paying Programming Languages

Are you interested in pursuing a career in AI programming? Then upGrad is the answer.

How upGrad Can Help You Master the Best Programming Languages for AI and Machine Learning?

In today’s rapidly advancing world of AI and machine learning, mastering the right programming languages is crucial for building efficient, scalable, and innovative solutions.

upGrad offers hands-on programming training with real-world projects, expert mentorship, and 100+ free courses. Join over 1 million learners to build job-ready skills and tackle industry challenges.

Here are some relevant courses you can check out:

Also, get personalized career counseling with upGrad to shape your programming future, or you can visit your nearest upGrad center and start hands-on training today!

FAQs

1. Which AI programming language is best for natural language processing (NLP)?

NLP applications are commonly built using Python due to its rich ecosystem of libraries like NLTK, SpaCy, and transformers.

2. Can I use Java for deep learning projects?

While Java is traditionally not the first choice for deep learning, libraries like Deeplearning4j and DL4J have made it possible to implement deep learning models effectively in Java.

3. Why is Python so popular for AI despite being slower than other languages?

Python's popularity in AI comes from its extensive libraries, user-friendly syntax, and large community, making it an easy choice for rapid prototyping and development.

4. What makes Julia a better option for AI compared to Python?

Julia offers superior performance, especially in numerical and scientific computing, due to its speed, which can be crucial for large-scale AI projects or simulations.

5. Is R a good choice for machine learning compared to Python?

While Python has a more extensive library ecosystem for machine learning, R is excellent for statistical analysis and data visualization, making it ideal for data-heavy projects.

6. How does Rust compare to Python for AI performance?

Rust can outperform Python in terms of execution speed and memory efficiency, but it lacks the robust AI-specific libraries and tools that Python offers.

7. What role does MATLAB play in AI development?

MATLAB is primarily used for algorithm development, simulations, and numerical analysis, making it popular in academia and research environments for AI model testing and prototyping.

8. Can AI models be developed without programming knowledge?

Yes, no-code or low-code platforms like Google AutoML and Microsoft Azure AI allow users to develop AI models without extensive programming knowledge, though having coding skills enhances flexibility.

9. How does Scala support AI in big data applications?

Scala is widely used in big data processing with frameworks like Apache Spark, making it highly suitable for large-scale machine learning projects and real-time data analysis.

10. Is Prolog still relevant for AI development?

Prolog is still valuable for AI tasks involving logic, rule-based reasoning, and expert systems, though it has been largely superseded by more mainstream AI languages.

11. How does Go support AI and machine learning development?

Go is suitable for AI projects that require high concurrency, scalability, and efficiency, especially in distributed systems, though it lacks as many specialized AI libraries as Python or R.