This study evaluates three machine learning clustering algorithms—K-Means, DBSCAN, and Hierarchical Clustering—for customer segmentation in the banking sector. Using a dataset of customer demographic, financial, and transactional data, we compare the algorithms based on the Silhouette score and Davies-Bouldin index. Hierarchical Clustering performed best, achieving the highest Silhouette score (0.68) and the lowest Davies-Bouldin index (1.15), indicating well-defined and compact clusters. K-Means showed reliable performance with a Silhouette score of 0.62 but required predefined clusters. DBSCAN identified noise effectively but resulted in lower cluster compactness, with a Silhouette score of 0.55 and a Davies-Bouldin index of 1.50. The findings highlight Hierarchical Clustering as the most effective method for customer segmentation in banking, with flexibility depending on the data and objectives.