This study investigates the use of machine learning to predict user purchase intentions based on behavioral data in a multi-category e-commerce platform. By analyzing seven months of user interaction logs—comprising product views, cart additions, and purchases—the research applies feature engineering to generate variables such as event weekday, product category levels, session activity count, and cart-to-view ratios. Four classification models were developed and evaluated: logistic regression, decision tree, random forest, and gradient boosting. Among these, the Random Forest algorithm outperformed the others, achieving the highest accuracy and F1-score, effectively balancing precision and recall. The results demonstrate that machine learning can reliably predict purchase intent and support more targeted marketing, personalized recommendations, and improved conversion strategies in e-commerce environments.