In today’s highly competitive retail environment, accurate product demand forecasting is vital for optimizing inventory management, minimizing operational costs, and enhancing customer satisfaction. This study, titled “Product Demand Forecasting Using Stacked Machine Learning Techniques,” presents a robust and scalable approach that integrates multiple regression-based algorithms through a stacked ensemble model to improve prediction accuracy and reliability. The proposed system employs a combination of models—including Linear Regression, Random Forest, XGBoost, and CatBoost—as base learners, with a meta-model strategically trained to combine their outputs for optimal forecasting performance. The model is trained on historical sales data enriched with store-level attributes, promotional activities, transaction counts, and temporal variables. Comprehensive data preprocessing, including missing value treatment, feature encoding, and scaling, ensures data integrity and model stability. Hyperparameter optimization using Optuna further refines the learning process for each algorithm component. By leveraging stacking, the approach effectively reduces the bias and variance limitations of individual models, achieving superior generalization on unseen data. Experimental analysis confirms that the stacked model outperforms standalone algorithms in predicting short-term product demand trends. A Streamlit-based interactive application was developed to visualize and forecast product demand, supporting both manual and batch (Excel-based) predictions. The results demonstrate the potential of ensemble learning for real-world demand forecasting applications, enabling informed decision-making in inventory control, promotion planning, and supply chain management. Future enhancements may incorporate external factors such as seasonality, economic conditions, and weather patterns for greater forecasting precision.