This study presents an integrated framework combining clustering (K-Means, DBSCAN), dimensionality reduction (PCA, UMAP), and association rule mining (Apriori, Eclat) to extract actionable insights from retail data. Using a Kaggle dataset of over 1,000 customer transactions, we identify three distinct customer segments: high-spending youth, older frequent buyers, and budget-conscious middle-aged shoppers. We link these segments to product affinities, such as the association between blouses and jewelry. Unlike prior studies treating these methods separately, our integrated approach enables cluster-specific marketing strategies such as personalized bundling and influencer-driven campaigns. We validate cluster robustness through multi-algorithm consensus and demonstrate UMAP’s effectiveness over PCA in capturing nonlinear demographic-spending relationships. The study also discusses limitations such as parameter sensitivity and data granularity, offering insights for future research and practical applications.