Online shoe shopping is challenged by the wide variety of brands and designs, making it difficult for consumers to make a decision. The inability to try on shoes before buying forces users to rely on information provided by manufacturers and reviews, which can be inaccurate. This reliance further complicates decision making, leading to frustration and discouragement. To improve the shopping experience, this project proposes a comprehensive solution with Web Scraping techniques using Scrapy and Selenium, along with a content-based recommendation system, designed to optimize the purchase decisions for sports shoes and reduce search time. The recommendation engine, implemented in Flask, uses data collected through Web Scraping and is presented through an interactive interface in ReactJs. The results show a significant improvement in decision making, offering personalized recommendations.