Published Paper Details:

The integration of artificial intelligence (AI) in food waste illustrates a transformational approach to promoting the principles of a circular economy. This study examines how Ai controlled technologies, food persecution, classification and redistribution can be optimized, including machine learning, computer vision, and predictive analytics. Ai driven systems enable real time monitoring, demand forecasting and efficient selection mechanisms, reducing waste at various stages of the supply chain. Although AI is used in a wide range of waste disposal areas, such as construction and recycling of electronic equipment, food management possibilities remain underdeveloped. Existing research demonstrates the effectiveness of AI in optimizing waste classification and recycling and demonstrating its applicability to food loss solutions. By integrating AI into IoT sensors and blockchain technology, it dramatically improves food surveillance transparency and traceability, promotes efficient resource recovery and minimizes reliance on landfills. This study highlights the need for Ai controlled innovation to improve food system sustainability, mitigate environmental impacts, and support cyclical economic goals. Future research should focus on developing solutions for AI applications tailored to food waste to ensure efficiency, cost-effectiveness and scalability for various sectors.