Why Gaussian mixture model uses Expectation maximization instead of Gradient descent?
Not all the parameters (e.g., the assignment parameters) for a Gaussian mixture model are smoothly differentiable, thus can not be fit with gradient descent.
Other use cases for the expectation–maximization (EM) algorithm are:

Clustering
Latent variable estimation
Missing data estimation