Recommender systems have become a vital component of modern e-commerce platforms for improving user experience and increasing product sales. As online shopping continues to grow rapidly, the challenge of presenting relevant items to customers has intensified, making intelligent recommendation mechanisms essential. Machine learning and data mining techniques have proven highly effective in addressing this challenge by analyzing user behavior patterns, purchase history, browsing activities, and product features. Machine learning models “learn” from large-scale datasets collected through user interactions, transactional records, and online activity logs to generate accurate personalized suggestions. Numerous machine learning techniques—such as content-based filtering, collaborative filtering, hybrid models, and deep learning—are widely used to predict user preferences and recommend suitable products. The main aim of this review is to support research focused on improving accuracy and relevancy in recommendation generation using machine-learning-based techniques. Our review suggests that these approaches outperform traditional recommendation methods and emphasize that their performance depends heavily on the quality and diversity of the data on which they are trained