There is a big overlap between Optimal Control and Reinforcement Learning, in case you didn't know.

Also Steve Brunton does a lot on the interface between control theory and ML on his channel: https://www.youtube.com/channel/UCm5mt-A4w61lknZ9lCsZtBw/pla...

Another thing to keep in mind is that having AI/ML surrogates which can evaluate expensive functions faster can also be integrated as an information source in model predictive control algorithms.

Exactly. ML models such as autoencoders can also be used for reduced order modeling / dimensionality reduction e.g. for MPC of fluid systems.

there's a lot of work in the broad area. most of it doesn't engage with the classical control theory literature (arguably it should).

some keywords to search for recent hot research would be "world model", "decision transformer", "active inference", "control as inference", "model-based RL".