A Gaussian Mixture Model (GMM) is a statistical model that represents a distribution of data as a combination of multiple Gaussian distributions, each with its own mean and variance. This approach is useful for clustering and density estimation, allowing the model to capture complex patterns in the data by fitting several overlapping normal distributions. In a GMM, each Gaussian component corresponds to a different cluster within the data. The model assigns probabilities to each data point, indicating the likelihood that it belongs to each cluster. This flexibility makes GMMs popular in various applications, including image processing and machine learning.