Machine Learning-Enhanced Shelf-Life Prediction Model For Packaged Foods Using Environmental Cues

Accurate prediction of shelf life is critical for ensuring food safety, reducing waste, and optimizing supply chain operations in the packaged food industry. Traditional shelf-life estimation methods rely on fixed expiration dates and laboratory-based testing, which often fail to account for dynamic environmental conditions experienced during storage and transportation. This paper proposes a Machine Learning–Enhanced Shelf-Life Prediction Model that leverages real-time environmental cues such as temperature, humidity, and storage duration to estimate the remaining shelf life of packaged foods. The proposed framework integrates data preprocessing, feature extraction, and supervised learning techniques to model the degradation patterns of food products under varying environmental conditions. Multiple machine learning models are evaluated to identify the most effective predictor of shelf-life degradation. Experimental results demonstrate that the proposed approach significantly improves prediction accuracy compared to conventional static expiry estimation methods. By enabling adaptive, data-driven shelf-life forecasting, the proposed model supports improved food quality management, waste reduction, and informed decision-making across food supply chains.