In today’s competitive market landscape, understanding the nuances of a customer base is critical for the success of targeted marketing strategies. Customer segmentation, a method of grouping consumers based on shared characteristics, enables companies to tailor their marketing efforts to specific segments, thereby increasing the relevance and effectiveness of campaigns. This study leverages unsupervised machine learning techniques to analyze customer demographics and purchasing behaviors, aiming to identify key segments that share similar attributes. By applying three distinct clustering algorithms—K-means, Hierarchical Clustering, and DBSCAN—this study examines different feature reduction and preprocessing techniques to optimize segmentation performance. The findings reveal distinct customer segments that can inform data driven marketing strategies, ultimately leading to enhanced customer engagement and increased sales. The research highlights the potential of clustering methods for businesses seeking to optimize their customer segmentation processes and strategically expand into new markets.