A novel approach to predictive analysis and demand forecasting within supply chain management, employing the Extreme Gradient Boosting (XGBoost) classifier. In response to the escalating complexity and volatility of supply chains, accurate demand forecasting is imperative for optimizing inventory, production scheduling, and overall operational efficiency. Traditional forecasting methods often struggle to capture the nonlinear relationships and intricate patterns inherent in supply chain data. Conversely, XGBoost offers a potent machine learning technique adept at handling nonlinear relationships and delivering robust predictions. The proposed framework involves data preprocessing, feature engineering, model training, and validation stages. Through a case study employing real-world supply chain data, we demonstrate the superior performance of the XGBoost classifier over traditional methods in terms of accuracy, robustness, and scalability. This study underscores XGBoost's potential as a valuable tool for demand forecasting in supply chain management, facilitating informed decision-making, optimized inventory management, cost reduction, and enhanced customer satisfaction. Furthermore, the framework's adaptability and extendibility make it applicable to diverse industries and domains, contributing to the advancement of supply chain management through the integration of machine learning techniques for more precise and efficient demand forecasting