The digital revolution in retail has transformed ecommerce into a sophisticated, data-centric industry, with supervised learning algorithms playing a crucial role in improving business performance. This review explores the effectiveness of various supervised learning methods in tackling the primary challenges encountered by online marketplaces, including customer segmentation, recommendation systems, sales forecasting, and fraud detection. The study assesses the effectiveness of models such as logistic regression, decision trees, and neural networks, considering their predictive precision, commercial influence, and ability to scale. Additionally, it explores limitations like data integrity issues, model overfitting, and difficulties in interpretation, while also highlighting emerging trends such as hybrid modeling approaches and techniques for real-time adaptation. By synthesizing current research, this review offers insights into both the achievements and constraints of supervised learning models in e-commerce, and proposes directions for future studies to enhance their application in a swiftly changing marketplace.