Really, the limit’s your imagination! But machine learning methods, generally, work well if you have:

• a well-defined objective (in other words, a clear, unambiguous criterion which tells you whether a classification is the right one or how far an estimate is from its true value)
• enough training data.

That’s true of a lot of different problems! Image, audio and text classification are all things which are applicable to a lot of real-world problems.

The key way to take advantage of ML is identifying problems where the goal can be clearly defined but the method is tricky. It’s not worth training a ML model to say whether something is red or blue – the average pixel value tells you that. But determining the class of an object (or reading text out of an image, or…) – these are problems where you can define what a success is unambiguously, but coming up with heuristics for the problem is harder. That’s where you’ll get most “bang for the buck” from ML methods!