The exponential growth of e-commerce platforms has amplified the need for robust data-driven strategies to understand and predict customer behavior. In this dynamic digital landscape, the synergy between advanced database management systems (DBMS) and data mining techniques offers unparalleled potential in transforming raw transactional data into actionable business intelligence. While traditional database architectures have enabled data storage and retrieval, they often fall short in supporting predictive analytics required for real-time decision-making and personalized marketing. This study explores the integration of advanced database management frameworks—particularly those optimized for high-velocity, high-volume data streams—with supervised data mining models to enhance customer behavior prediction in e-commerce environments. By leveraging relational and NoSQL databases in tandem with data preprocessing techniques, the research addresses the challenges of data variety, sparsity, and inconsistency that often compromise model accuracy. The paper further investigates classification algorithms such as decision trees, support vector machines, and ensemble learning to segment customers based on purchase patterns, churn likelihood, and conversion probability. A key contribution of the research is the design of an optimized pipeline that facilitates seamless interaction between database systems and supervised machine learning workflows, enabling faster query execution, real-time analytics, and dynamic customer profiling. Case studies from leading e-commerce platforms are used to validate model performance and highlight operational scalability. The findings underscore the strategic importance of integrating intelligent data management with predictive analytics to drive personalization, retention, and revenue growth in digital marketplaces.