Machine learning is progressively being adopted by e-commerce platforms to enhance the shopping experience for consumers. By utilizing machine learning, large user datasets are analyzed to effectively forecast customer preferences, allowing for more relevant and personalized recommendations. Techniques such as collaborative filtering predict interests based on groups of similar users, while clustering, or segmentation, is employed for both users and items. This approach helps mitigate issues related to data sparsity and the cold-start challenge when it comes to generating valuable recommendations. Representation learning, particularly through deep neural networks, can capture complex patterns, which lead to high-quality recommendations. Additionally, LightGBM has shown enhancements in performance efficiency and its ability to manage very large data sets effectively. Hybrid models combine collaborative filtering with content-based filtering to achieve greater precision in recommendations. This review discusses cutting-edge ecommerce recommendation systems and how these advanced machine learning strategies work together to improve customer satisfaction, drive sales growth, and enhance competitiveness in the evolving e-commerce landscape.