The growing popularity of e-commerce platforms has transformed consumer behavior, with modern buyers increasinglyrelying on digital channels to compare prices before making purchases. However, manual price checking across multiplewebsitesremains inefficient, time-consuming, and error-prone. This research presents a Retail Purchase Intelligence System, anautomatedpricecomparison framework that aggregates product pricing information from various e-commerce sources and displays it inaunifiedinterface. The system utilizes web scraping techniques through Python libraries such as Beautiful Soup and Requests, combinedwithacentralized MySQL database for structured data storage. A lightweight front-end interface built with HTML, CSS, and JavaScript enablesintuitive search and quick visualization of comparative results. Experimental validation demonstrates that the systemcanaccuratelyextract and normalize pricing data across multiple online retailers, significantly reducing consumer effort and time infindingoptimaldeals. The proposed model also outlines scalability for dynamic websites through Selenium-based scraping and highlightsfutureextensions such as price-trend analysis, alert notifications, and browser integration. Overall, the system provides an effective, low-costsolution for real-time price intelligence and contributes to advancing consumer-centric automation in digital retail. Keywords:E-commerce, Web Scraping, Price Comparison, Python, Data Aggregation, Consumer Intelligence, OnlineRetail,Automation