Personalized product recommendations have become a cornerstone in elevating user satisfaction and boosting e-commerce sales. The advent of Machine Learning Techniques has empowered the creation of personalized product selection models. This research embarks on an exploration of the development and deployment of ML-based algorithms tailored to generate personalized product recommendations. The primary objective is to enhance user engagement and bolster conversion rates. This study harnesses use]. Recommendations-encompassing elements like browsing history, purchase records, and demographic information to construct precise and effective recommendation models. Recommendation systems heavily depend on user data, aggregating past interactions, behaviors, and preferences, which are continuously gathered and archived for comprehensive analysis. Recommendation systems come in various forms. In contrast, content-based filtering considers item attributes and user preferences.