The fashion e-commerce industry has experienced rapid growth, driven by advancements in technology and changing consumer behaviors. This study explores the application of machine learning (ML) techniques to enhance the operations of an e-commerce company specializing in fashion trading. We propose a comprehensive framework that leverages ML for demand forecasting, personalized recommendations, dynamic pricing, and fraud detection. Using a dataset of customer transactions, browsing history, and product metadata, we train models such as Random Forest, Neural Networks, and Gradient Boosting to optimize business processes. The models are evaluated based on accuracy, precision, recall, and F1-score, with SHAP values employed for interpretability. Our results demonstrate that ML-driven strategies significantly improve customer satisfaction, operational efficiency, and profitability. This research provides actionable insights for stakeholders aiming to integrate AI into fashion e-commerce platforms.