(untitled) (Contemporary Journal of Social Science Review, 2024)
A major global challenge in supply chains is food spoilage which causes major safety problems and both financial and waste-related issues. The conventional spoilage detection systems prove not only ineffective but also respond after spoilage has already occurred. This paper evaluates how cloud-based machine learning (ML) methods foretell food spoilage while optimizing supply chain safety protocols. Cloud computing platforms connected to IoT sensors collect data about environmental conditions including temperature and humidity and product status which permits real-time continuous monitoring and analysis. Predictive supply chain operations and spoilage patterns work together through supervised learning and time-series forecasting models. The study examines how these AI models enhance spoilage prediction accuracy as well as minimize waste along with maintaining adherence to food safety regulations. This type of cloud-based system delivers real-time visibility and enhances logistics efficiency which diminishes product damage while products are moved and stored. Research outcomes show that prediction accuracy along with food safety becomes better with the implementation of cloud-based ML systems. The paper finishes by explaining food industry consequences together with the cost reduction and sustainability advantages found in cloud machine learning yet facing challenges in data defense and system connection.