International Journal of Scientific Research and Engineering Development

The rapid growth of e-commerce has provided consumers with a wide range of products and purchasing options, but comparing prices, discounts, product quality, reviews, and availability across multiple platforms remains time – consuming and difficult. This study presents an AI-powered real-time price comparison system designed to provide users with accurate, personalized, and data-driven purchasing assistance. The proposed system integrates real-time web scraping and intelligent product matching to collect and align product information from multiple e-commerce platforms, addressing differences in product names, formats, and website structures. Machine learning techniques are incorporated for price prediction and purchase-timing analysis, enabling users to understand price trends, review and sentiment analysis, product ratings, discounts, availability, and brand-related factors to provide comprehensive product evaluation. Image-based product identification can also support faster product discovery, while automated price-drop alerts help users monitor desired products. In addition, adaptive scraping and analytical dashboards can improve the reliability, visualization, and interpretation of continuously changing ecommerce data. Privacy-preserving approaches such as federated learning provide potential support for collaborative pricing intelligence without directly sharing sensitive data. By integrating these capabilities into a unified and user-friendly platform, the proposed system aims to reduce manual comparison, improve purchasing decisions, identify genuine value beyond price alone, and provide timely and personalized insights for online shoppers.

Prof. Bharati Rathod, Nayana S, Sinchana G K, Umadevi, Varshini P, "Comparative Study On Smart Buy" International Journal of Scientific Research and Engineering Development, V9(4): Page(1388-1397) July-August 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.