Gaussian Mixture Models (GMMs) are probabilistic models that assume all data points are generated from a mixture of several Gaussian distributions with unknown parameters. They are widely used for clustering, density estimation, and pattern recognition, providing a flexible way to model complex data distributions by capturing subpopulation structures within an overall population.