AI-Powered Food Waste Reduction: A Data-Driven Approach to Sustainable Consumption

Food waste is a critical global issue that impacts food security, economic stability, and environmental sustainability. This paper explores the role of Artificial Intelligence (AI) in reducing food waste through data-driven strategies. By leveraging machine learning algorithms, predictive analytics, and IoT-enabled monitoring systems, AI can optimize food supply chains, enhance inventory management, and predict consumer demand more accurately. The study examines AI-based approaches such as image recognition for food quality assessment, smart waste tracking, and AI-powered recommendation systems for surplus food redistribution. Additionally, the integration of AI with big data analytics enables real-time insights into food consumption patterns, helping businesses and consumers make informed decisions to minimize waste. The research highlights case studies where AI has successfully contributed to reducing food waste in households, restaurants, and supply chains. Challenges such as data privacy, implementation costs, and scalability are also discussed. The findings suggest that AI-driven solutions can significantly contribute to a more sustainable and efficient food management system, ultimately supporting global efforts toward achieving zero food waste.