Customer segmentation is very important in today’s marketing environment to ensure that appropriate resources are utilized as businesses expand. Our project considers the customers’ segmentation through K-Means clustering and Gaussian Mixture Models (GMMs). We used the Mall Customer Dataset. To determine the optimal number of clusters for K-means, we used the elbow method, while for GMM, we used the Bayesian Information Criterion (BIC). By adding eXplainable AI (XAI) features like SHAP and LIME, we were able to enhance interpretability of GMM. The research findings indicate that GMM with XAI offers greater insight that enables business organizations to achieve higher performances.