Top 3 Programming Languages to Learn for AI [2026]

The term artificial intelligence was coined in 1956 at Dartmouth College. In the following 15 years after that, AI thrived as computer power increased. But soon, we hit the limit, and stagnation set in.

Luckily, technology is progressing rapidly. We’re now living in a world of chat assistants that write code, image models that paint on demand, and recommendation engines behind every app on your phone. Since 2022 the field has moved faster than at any point in its history, and it isn’t slowing down.

Good on you for wanting to get into a field that’ll be in high demand in the future!

But where to start, right? What programming language is the best for AI?

AI consists of algorithms based on mathematical principles, and those work in all languages. That said, some languages are better for programming AI than others because they may be faster, have a great community, helpful libraries, and other benefits.

In the next 6 minutes, you’ll learn about the top 3 programming languages for AI in 2026 and an additional five for more in-depth knowledge.

I’ll also talk about some awesome projects that have come to life thanks to AI.

A word of caution: it’s going to be heavy on the jargon. If you don’t understand something, scroll down to the FAQ to read the definition.

The original version of this article was reviewed by the CEO of an AI start-up before publication. It has since been refreshed for 2026 with the current state of the tools.

1. Python

Python is an interpreted, high-level, object-oriented programming language that you can use for almost any project.

Python is the language of AI. PyTorch, TensorFlow, scikit-learn, Hugging Face and the official SDKs for ChatGPT, Claude and Gemini are all Python first, and it has a huge community behind it. That makes it the ideal starter language!

Thanks to community support, you have a massive amount of libraries and frameworks for AI development at our disposal. Use what others already created, instead of coding it all from scratch.

Fun fact: the creator loved Monty Python so much that he named the language after the comedy group. Less fun fact: since 2024 Python has also been the most used language on GitHub, overtaking JavaScript, almost entirely because of AI and data work.

Why You Should Learn Python:

• You always have support from the vast community.
• You have a ton of ready-made libraries at your disposal
• The syntax is easy to understand.
• Coding something in Python is faster than most other languages.

Where to Learn Python:

This Udemy course is made for people like you wanting to learn Python for AI. The course includes R as well.

If you have never programmed at all, start with a plain Python course first. Our best Python courses guide lists the ones students rate highest.

2. R

R is an object-oriented language created to explore, model, and visualize massive amounts of data. It’s all about statistics and data science.

R is a general language, like Python, but it features numerous packages in the field of ML. Learning R will help you take your project to the next level by being able to interpret and use large data sets.

Why You Should Learn R:

• You can effectively work with big sets of data, which you need for powerful AI.

Where to Learn R:

With the Udemy course linked above, you’ll learn R and Python for AI in one go.

3. C

C is a low-level procedural, compiled language you can use for anything. It’s not an easy language, but it’s a popular and powerful one.

C can do some heavy lifting because it’s a low-level language. Search engines run on C code because they need to be crazy fast. So do games that require a lot of computing power.

Remember how I was talking about libraries you can just load into your Python code? Most of them are written in C, too. You don’t have to make your own libraries to be successful as an AI developer. But if you want to, C is the way to go.

Why You Should Learn C:

• It’s ideal for projects that need to be fast in execution.
• If you don’t find an existing library to solve your problem, you can create one.
• It works great with Python.

Where to Learn C

This Udemy course is great for beginners who want to start mastering C.

Other Languages to Learn for AI

As I said, you can use any language to create AI. Those three are just my personal favorites. If you know your way around all of them, you’ll be well off.

But those languages may not be for you, so here are some alternatives:

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SQL is a programming language to store and retrieve data from tables. When we move away from theory, SQL becomes an important language for AI developers.

Julia has a far smaller community and fewer libraries. Because of this, Julia is more challenging to get into than Python. But it has a loyal following in scientific computing for a good reason: it’s faster than R and Python and specially laid out for numerical work and machine learning.

Lisp was created by the father of AI, John McCarthy. That’s why many consider it to be the best language for AI. I don’t believe that holds because, in the 60 years that Lisp has been around, other languages caught up to and surpassed Lisp in the field of artificial intelligence. However, it’s still handy thanks to its prototyping capabilities and symbolic processing.

Prolog is like Lisp, a vintage language. It’s surpassed by many languages today but has some useful tools for AI programming like pattern matching, automatic backtracking, and tree-based data structure mechanism.

Matlab is a high-performing programming platform made for engineers and scientists. Many of the methods you’ll use for your project come ready-made in Matlab. It’s relatively easy to learn, but it lags miles behind Python in terms of programming features. It’s also expensive.

Honorable mentions for 2026: Rust is where a lot of the new high-performance AI tooling is being written (fast tokenizers, vector databases, inference servers), and JavaScript/TypeScript is what you’ll use to put an AI feature in front of users, since most chat interfaces and agent SDKs ship for it first. Neither is where you start, but both are worth knowing about.

AI in the Real World (Try It Out Now!)

Five years ago the fun AI demos were research toys. Today they are products you probably already use. Here are three you can try in a minute, and each one is a good illustration of what the languages above are used for.

Chat assistants like ChatGPT, Claude and Gemini are large language models: neural networks trained on a huge slice of the internet that can answer questions, write code and explain concepts. Ask one which language to learn for AI and you will get roughly the advice in this article (we checked).

Image generators like Midjourney, DALL-E and Stable Diffusion turn a sentence into a picture. The original version of this article showed off AttnGAN, a 2018 research model that struggled with anything but birds. Its descendants now produce photorealistic images in seconds, and Stable Diffusion is open source, so you can run it yourself with a few lines of Python.

This Person Does Not Exist is still online and still equally fascinating and disturbing. Every time you reload the page, you see a new human face. The twist is, none of these people exist, and they never have. The face you’re looking at is entirely generated by a neural network called a GAN, the technology that image generators grew out of.

Want to build things like these rather than just use them? The machine learning and deep learning categories on CourseDuck rank the courses students rate highest, and our five best machine learning courses roundup picks the top ones.

Conclusion: What the Future Holds

Old languages like Lisp and Prolog still have their admirers, but they are not coming back. More modern languages have surpassed them and continue to grow rapidly.

When the first version of this article was written, Julia looked like it might one day challenge Python. It hasn’t happened. The generative AI boom made Python more dominant, not less, because every major model, library and SDK arrived in Python first. Julia remains a great tool for scientific computing, and Rust is taking over the performance-critical plumbing underneath, but the front door to AI is still Python.

For now, just focus on learning the basics. The field is moving fast, but the fundamentals (Python, a little math, and how to read data) have not changed, and they transfer to whatever comes next.

You’ve discovered what AI coding language you can start with and where to study them. It’s time to get to work and have fun!

FAQ

What Is AI?

AI is short for artificial intelligence. The term covers everything related to making smart machines that can creatively solve problems without human input.

What Is ML?

ML is short for machine learning. It’s a subset of artificial intelligence and refers to algorithms that can be trained and improve themselves without additional coding.

What Is DL?

DL is short for deep learning. It’s a subset of machine learning built on neural networks with many layers. Given enough data and computing power, these networks learn patterns on their own, which is how today’s chat assistants and image generators work.

What’s a Neural Network?

Neural networks mimic the human brain. It’s a collection of algorithms that can interpret vast amounts of data.

What’s a Library?

A library in programming is a collection of pre-made code. Instead of coding the same things again and again, programmers can simply integrate libraries.

What’s a Low-Level Programming Language?

Low-level means that the programming language is closer to machine language and farther away from human language. Code written in low-level languages doesn’t need to be translated into computer language and is, therefore, faster in execution than high-level languages.

What’s a High-Level Programming Language?

High-level means that the programming language is closer to human language than to machine language (zeros and ones). It’s easier to write in high-level languages because they come more naturally. Code written in high-level languages needs to be translated to machine language and is, therefore, slower in execution than low-level languages.

What Does Compiled Mean?

Compiled means that a code file written by a human gets run through a program, the compiler, and is then readable by a machine.

What Does Interpreted Mean?

Interpreted means that the code gets translated into machine language every time you run it.

What Does Object-Oriented Mean?

Object-oriented programming (OOP) makes use of, well, objects. Those objects are bits of code in different files that can be reused and integrated wherever you want them in your code. It’s opposed to procedural programming where a computer would just run through the code line by line.