A Gaussian Mixture Model (GMM) is a statistical model that represents a distribution of data as a combination of multiple Gaussian distributions. Each Gaussian component has its own mean and variance, allowing the model to capture complex patterns in the data. GMMs are particularly useful for clustering, where they can identify groups within a dataset based on similarities. In a GMM, each data point is assigned a probability of belonging to each Gaussian component. This probabilistic approach enables more flexible modeling compared to traditional clustering methods like k-means. GMMs are widely used in various fields, including image processing and speech recognition.