I guess pared down to its absolute core, this is what Monte Carlo is - you just generate many a large ensemble of possible states.
But this simplified explanation misses out on one key aspect of Monte Carlo: sometimes different kinds of Monte Carlo moves can be designed that can allow it to more efficiently sample the phase space than other methods such as gradient descent.
Unfortunately, doing so is can be very involved, and is not always very general, so it isn't as easy to do as using other methods for exploring phase space.
Your answer feels like its saying "its only partly random, you use the outcome of your experiments to walk the space of possible solutions more carefully, in the light of experience" which would make it more like GLMM or something markov-y
But this simplified explanation misses out on one key aspect of Monte Carlo: sometimes different kinds of Monte Carlo moves can be designed that can allow it to more efficiently sample the phase space than other methods such as gradient descent.
Unfortunately, doing so is can be very involved, and is not always very general, so it isn't as easy to do as using other methods for exploring phase space.