Food waste is a critical global challenge with profound economic, environmental, and social consequences. The increasing volume of wasted food exacerbates resource inefficiency, contributes to greenhouse gas emissions, and strains waste management infrastructure. To address these issues, smart food waste management systems (SFWMS) have emerged, integrating advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and machine learning (ML) to minimize waste generation, optimize resource utilization, and enhance sustainability across the food supply chain.

This paper examines the current state of food waste management, reviews existing literature on technological interventions, and proposes a novel smart food waste management framework. The study investigates the role of automation, real-time data analytics, and consumer awareness in reducing food waste at various stages—from production and distribution to retail and household consumption. The proposed framework leverages IoT-enabled sensors for waste tracking, AI-driven predictive analytics for demand forecasting, and ML algorithms for dynamic waste sorting and recycling optimization.

Key findings indicate that smart food waste management systems can significantly improve operational efficiency, reduce economic losses, and support the achievement of Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production). The paper also discusses challenges in implementation, including technological barriers, cost considerations, and behavioral factors. By fostering collaboration among stakeholders—governments, businesses, and consumers—smart food waste management systems can pave the way for a more sustainable and circular food economy.