Surplus cooked food from restaurants, hotels and cafés is often discarded because donors cannot verify its safety or reach an NGO in time. This paper presents a Smart Food Donation Management System that combines low-cost IoT sensing, explainable machine learning and automated NGO allocation in one web platform. An ESP32 node with DHT22 and MQ-135 sensors streams storage conditions, and high gas readings flag or block spoiled food. Six algorithms were compared with 5-fold cross-validation; the selected Gradient Boosting model classifies each donation as low, medium or high risk from temperature, humidity, food age and food type. A safe-time forecast estimates when food will become high risk, and exact Shapley values explain each prediction. An allocation module offers each donation to one NGO at a time, filtering by remaining capacity and by whether the NGO can arrive before the food becomes unsafe. On 600 heldout records the model achieved 97.0% accuracy, a macro F1-score of 0.957 and 98.7% high-risk recall, with no high-risk record classified as low risk. In a 50-day simulation the allocation method delivered 87.2% of meals safely, against 83.3% for nearest-NGO allocation, and all 45 functional tests passed.

S. Mathavencie Sudhakar, Dr. Manoj Singh, "An AI- and IoT-Enabled Smart Food Donation Management System with Explainable Food-Risk Prediction, Safe-Time Forecasting and Automated NGO Assignment" International Journal of Scientific Research and Engineering Development, V9(5): Page(1376-1385) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.