A Digital Framework for Efficient Food Donation Management

Abstract

Food wastage and inefficient distribution of surplus food continue to be major social challenges, especially in developing countries, where large quantities of edible food are discarded while many people face food insecurity. To address this gap, this paper proposes a AI Integrated digital framework for efficient food donation management using the MERN stack. The proposed system provides a centralized web-based platform that connects food donors such as restaurants, event organizers, and households with verified NGOs and volunteers in real time. Using the MERN stack (MongoDB, Express.js, React.js, and Node.js) and Gen AI, the framework ensures scalability, secure data handling, and responsive user interaction. Key features include donor and receiver registration by admin that ensures user safety check , real-time food request and acceptance, location-based matching, status tracking, and administrative monitoring to ensure transparency, authenticity and accountability. Unlike traditional manual or semi-automated donation systems, the proposed framework minimizes delays, reduces food spoilage, and improves coordination among stakeholders. The system also supports data analytics for tracking donation patterns and measuring social impact. Overall, this digital framework aims to reduce food waste, enhance operational efficiency in food donation processes, and contribute to sustainable social welfare through modern web technologies.

Introduction

Food wastage is a major global issue with serious social, economic, and environmental impacts, while many people still face hunger. Traditional food donation methods are manual, uncoordinated, and lack real-time tracking, leading to inefficiencies, delays, and increased spoilage.

To address these challenges, the paper proposes a digital food donation management system built using the MERN stack with AI integration. The system provides a centralized platform that connects donors (restaurants, households, events), NGOs, and volunteers. Donors can upload surplus food details, NGOs can accept donations, and volunteers handle delivery, all with real-time updates and transparent coordination.

The platform integrates advanced technologies such as Retrieval-Augmented Generation (RAG), a large language model (Gemini), and a vector database (Pinecone) to support an AI-powered chatbot. This chatbot assists users by answering queries related to food safety, freshness, and availability, improving communication and user experience.

The system workflow includes user registration, food posting, NGO acceptance, volunteer delivery, and real-time status monitoring by administrators. Compared to traditional methods, the system reduces manual effort, response time, and food spoilage while improving transparency and efficiency.

Results show that the AI-integrated system performs better in scalability, reliability, and coordination. However, future improvements may include mobile app development, microservices architecture, computer vision for food quality assessment, blockchain for transparency, and IoT-based monitoring for food safety.

Conclusion

This project focuses on solving the problem of food wastage by providing a simple and effective digital solution for food donation management. By using anAI-Integrated MERN stack–based web application, the system makes it easy for donors to share surplus food, for NGOs to receive it on time, and for volunteers to help in delivery. The platform reduces manual effort, improves coordination, and ensures transparency in the donation process. Overall, the project shows how modern web technologies can be used practically to support social welfare and reduce food waste in an efficient and meaningful way.

References

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Copyright

Copyright © 2026 Syamala Devi, Prameela , Harini , Manikanta , Dr. B. Satyanaryana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.