In the evolving retail landscape, businesses must adapt to changing consumer behavior to remain competitive. Our project, Optimizing Retail Strategy through Machine Learning-Based Customer Segmentation, focuses on empowering John Keells, a leading supermarket chain in Sri Lanka, to enhance its marketing effectiveness through data-driven insights. With operations across 20 locations and a wide product range—including dry goods, fresh produce, and luxury items—John Keells faces the challenge of addressing diverse customer needs using traditional, generalized marketing strategies. To overcome this, we developed a machine learning-based solution that segments customers according to their purchasing behavior using historical sales data. The process involved comprehensive data preprocessing, including cleaning, feature engineering, and normalization, followed by model development. We evaluated multiple classification algorithms, including Random Forest, XGBoost, LightGBM, CatBoost, and Neural Networks. These insights enable John Keells to adopt highly targeted and personalized marketing campaigns, improve customer retention, and optimize inventory planning. The solution also provides a foundation for future CRM and marketing automation efforts. This project showcases how machine learning can transform traditional retail strategies into intelligent, customer-focused approaches that drive business growth.