Pros: Has the ability to find more local clusters that K-Means would not be able to differentiate
K-Means aims to minimize the Within Cluster Sum of Squares, while EM aims to maximize the likelihood of an underlying probability distribution.
When used for clustering, any of the evaluation metrics (Silhouette Score, Dunn Index, Rand Index, etc.) are appropriate
A Gaussian Mixture Model describes an underlying distribution that is composed of multiple individual Gaussian distributions
Expectation-Maximization refers to a two-step, iterative process that is often used when latent or unobserved variables are present underlying a data generation process.