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In the rapidly evolving landscape of digital commerce, personalized recommendation systems have emerged as essential tools for enhancing customer experience and driving business growth. This project presents the design and implementation of an intelligent Product Recommendation System that leverages retail transaction data to deliver customized product suggestions based on customer purchasing patterns. Using the Online Retail dataset from the UCI Machine Learning Repository, the system integrates Recency, Frequency, and Monetary (RFM) analysis with unsupervised learning algorithms such as K-Means, Agglomerative Clustering, and DBSCAN to segment customers into meaningful groups. Extensive data preprocessing, including handling missing values, removing anomalies, and normalizing features, was conducted to ensure data quality. Exploratory Data Analysis (EDA) provided insights into top-selling products, customer distributions, and seasonal purchasing trends. The system’s performance was evaluated using visualization methods and Silhouette Score metrics, confirming the effectiveness of the clustering models. Furthermore, the solution was deployed using a Streamlit-based interactive web application, enabling real-time visualization of customer segments and personalized product recommendations. By reducing decision fatigue and supporting data-driven business strategies, the proposed system demonstrates a scalable and practical framework for enhancing user engagement, optimizing marketing strategies, and improving customer retention in e-commerce platforms