A Gaussian Mixture Model assumes data is generated from a mixture of several Gaussian distributions with unknown parameters. It is an unsupervised clustering method often trained with the Expectation-Maximization algorithm. GMMs can model more complex clusters than K-Means by allowing varying shapes and sizes.