Eh to a certain degree. The architecture of the models is very much control theory and then the training is control system tuning (which of course is an optimisation problem like you said).

I would definitely agree that optimisation fits the definition in part but I find really only control theory covers that entire field of signal processing, optimisation, and decision making systems.

And importantly, because ML in some amount touches on all of those, control theory tends to fit better as it focuses so heavily on providing a comprehensive framework for reasoning about all of those elements together.