The rapidly growing global population and evolving consumer preferences have intensified challenges in managing perishable food inventories and minimizing post-harvest losses. “Advanced Deep Learning Techniques for Shelf-Life Estimation and Food Waste Reduction” presents a comprehensive investigation into state-of-the-art neural network architectures and methodologies designed to accurately forecast the remaining usable life of fresh and processed food products. By integrating multimodal data sources—encompassing physicochemical quality indicators, environmental storage conditions, and real-world supply-chain variables—this research demonstrates how deep learning models can transform traditional shelf-life prediction and demand-planning practices, yielding substantial reductions in food spoilage and waste. Finally, we discuss broader implications for sustainable food systems and circular economy practices. By providing granular, product-level shelf-life insights, advanced deep learning enables more precise allocation of near-expiry goods to secondary markets (e.g., discount retail and food bank donations) and optimizes routing to minimize carbon emissions associated with spoilage-induced inefficiencies. Life cycle assessment (LCA) modeling of the pilot studies estimates a 7% reduction in greenhouse gas emissions per unit of food distributed, driven by lower waste volumes and reduced need for expedited transport of replaced stock.

In conclusion, this research substantiates that advanced deep learning techniques—particularly hybrid CNN-LSTM architectures integrated with reinforcement learning–driven decision systems—offer a transformative approach to shelf-life estimation and food waste reduction. The demonstrated improvements in predictive accuracy, operational waste savings, and environmental benefits underscore the potential for wide-scale adoption in commercial food supply chains. Future work will explore federated learning strategies to enable collaborative model training across organizations without sharing proprietary data and the extension of these methodologies to additional food categories and distribution modalities.