Pretty broad question, but here's one application.

ML is about performing brute force search within a large data set and analyzing trends that converges upon an answer. Then building off on that to further improve the model.

An example is like chess. You can program in the chess rules very easily, i.e. knights move in an L shape, if a king is in check, it must move to safety etc.

Creating an AI to play chess is the Machine Learning part.

The dumbest possible chess playing strategy is to move pieces (within their constraints) at random. So you can run chess games simulations where two AIs move randomly. Then analyze the games where black won and the games where white won to see if there was any common pattern for victory. It may be that games where either side started with a knights open resulted in a victory indicating that is a strategic move. Then you can update the AI to incorporate that strategy for future games. Then repeat the chess games simulations and continue to find trends resulting in victory and continue to incorporate new strategies into the AI.