H Sreeja, Akhil Reddy, Tharun Teja Rao, P Chandra Kashiyap, Dr. R. Santhoshkumar 03 05 Demand forecasting plays a crucial role in effective decisionmaking across industries such as retail supply chain and inventory management This work presents a datadriven machine learning framework for predicting shortterm and longterm product demand using multiple algorithms The system is designed to compare the performance of traditional and advanced models including Random Forest Gradient Boosting LSTM and XGBoost Initially the dataset is preprocessed through normalization shuffling and splitting into training and testing sets to ensure reliable evaluation Each model is trained on 80 of the data and tested on the remaining 20 to measure performance The evaluation is conducted using metrics such as R score for accuracy and Root Mean Square Error RMSE for prediction error analysis Experimental results show that Random Forest and Gradient Boosting provide moderate accuracy while LSTM captures temporal patterns more effectively However the XGBoost model outperforms all other methods by achieving the highest accuracy and lowest error rate The system also includes a user interface that enables realtime demand prediction for selected products The results demonstrate that advanced ensemble techniques can significantly enhance forecasting performance This approach provides a reliable and efficient solution for demand prediction in realworld applications The proposed system can support better planning reduce uncertainty and improve operational efficiency Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026