ML Models are usually used in the context of prediction, Y = F(X,θ), where X = inputs, θ = weights, F = model. There's typically no explicit feedback look (only historical data), no time-variation (you can add using lags however), and no existing model structure in most cases (most are black boxes, some like linear regression have a linear model which are fairly loose).

Kalman Filters are used in the context of a very specific model-type for dynamic systems (a state-space model, see below) to update states (xₖ) using feedback data from sensors (yₖ). These state-space models can either be derived by fitting data, or they can be derived from first principles through physics equations.

  xₖ₊₁ = f(xₖ) + g(uₖ)

  yₖ = h(xₖ)

The feedback loop is modeled explicitly, including any control actions (uₖ) that you took to affect the environment.

For instance, when driving a car, examples of states (x) are position/velocity/acceleration (which might not be directly measured with a sensor! But can be backed out from a mathematical model from quantities that are measured), sensor measurements (y) might be speedometer, accelerometer readings, and control actions (u) might be throttle position, brake pressure, steering angle. The Kalman filter has a model relating all this in time, and based on that model and sensor readings, it reconstructs/infers the likeliest states in the presence of even noisy measurements. This is why Kalman filters are known as "state estimation" algorithms.

ML models typically do not do this -- they only predict. Kalman filters predict and update.