Applications of Artificial Intelligence in the Food Supply Chain: Prediction and Optimization: A Systematic Review

Abstract

The food industry faces growing challenges in efficiency, sustainability, and adaptability to variable demands. Artificial intelligence (AI) is emerging as a transformative solution for the supply chain, optimizing demand prediction, quality control, and waste reduction. This Systematic Literature Review analyzes studies published between 2020 and 2025 on AI applications—such as machine learning, deep learning, and explainable models—in the food industry. Using the PICOC approach and the Scopus database, 49 relevant studies were selected. The results show that AI improves key stages: shelf-life prediction, automation with computer vision, and monitoring through digital twins, increasing efficiency and sustainability. However, limitations persist, such as accessibility for small businesses and the need for more transparent models. This research provides a structured overview of the use of AI, identifies predominant technologies, and suggests future lines of work focused on scalability and explainable AI, adding value to agrifood management.