Food waste and food insecurity are closely connected problems, yet existing technological solutions often address only one part of the redistribution process. This literature review examines six studies covering digital food donation, NGO coordination, food-spoilage prediction, food-waste prediction, geographic redistribution, and AI-supported resource allocation. The reviewed systems range from lightweight web applications using Firebase to integrated platforms using machine learning, geospatial services, blockchain, recommender systems and online optimization. The comparison shows that digital donation platforms can improve visibility and coordination, while AI models can add prediction and prioritization capabilities. However, important limitations remain: some systems do not use quantitative datasets, some rely on small or simulated datasets, several prototypes have limited real-world validation, and AI-based systems introduce issues related to data availability, bias, privacy and deployment. The review identifies a research gap in combining donor-NGO matching, food safety or waste prediction, location-aware prioritization, real-time coordination and practical role-based workflows within one deployable system. These findings provide a foundation for designing an integrated food redistribution platform such as Waste2Meal.

Key Words: Food Waste Management, NGO Donation Systems, Food Redistribution, AI-Based Matching, Food-Rescue Optimization