The rapid growth of online commerce has transformed how businesses interact with customers, pushing innovation in personalized shopping experiences. An e-commerce platform with an integrated recommendation engine significantly enhances user engagement, drives sales, and improves customer retention by offering personalized product suggestions. This research article explores the design, development, and implementation of an e-commerce platform equipped with a state-of-the-art recommendation engine using collaborative filtering, content-based filtering, and hybrid approaches. Key contributions include the architecture of the platform, algorithmic choices, system evaluation, and discussion on scalability and performance metrics. Experimental results demonstrate that integrating recommendation systems can improve click-through ratings (CTR), conversion rates, and average order values. The article includes methodological frameworks, comparative analysis, and design patterns for practitioners and researchers planning to adopt or improve recommendation systems in e-commerce. The platform leverages user behavior data and product attributes to generate accurate and relevant recommendations in real time. Emphasis is placed on optimizing algorithm efficiency to handle large-scale data and ensure seamless user experience. Additionally, the system incorporates feedback mechanisms to continuously refine recommendation quality and adapt to evolving user preferences.