If you can make certain assumptions about the system (mainly that sources of noise follow gaussian distributions and are independent), then the Kalman filter gives the best possible estimate of the system state. And it can be computed cheaply, like on the Apollo guidance computer.

You basically need to know some kind of a model for the system to run KF. Whereas ML is all about working out the model automatically.

As for similarities, KF is a really efficient implementation of Bayesian inference. I think that any ML model that isn't fundamentally using Bayesian inference, is fundamentally flawed.