Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 8 — Issue 1 — March 2026

Food Waste Prediction using Random Forest and Redistribution System

https://doi.org/10.36548/jucct.2026.1.002
Vineet Kumar
Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur
Arun A.
Associate Professor, Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur
Devansh Om Saxena
Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur
Saksham Uniyal
Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur
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Kumar, Vineet, Arun A., Devansh Om Saxena, and Saksham Uniyal. 2026. “Food Waste Prediction Using Random Forest and Redistribution System”. Journal of Ubiquitous Computing and Communication Technologies 8 (1): 17-36. https://doi.org/10.36548/jucct.2026.1.002.
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Keywords

— Artificial Intelligence
— Random Forest
— Food Waste Management
— Machine Learning Prediction
— Location-Based Services
— Food Redistribution System
Published: 14-02-2026

Abstract

Food wastage is a critical global issue results in significant environmental degradation, economic loss and food insecurity. This paper proposes an AI-enabled food waste prediction and redistribution system will help to reduce food wastage by smartly connecting restaurants and food donors with NGOs nearby. A proposed system integrates a web-based architecture with comprehensive machine learning, geospatial intelligence and real-time communication mechanisms to enable efficient food redistribution. This system predicts wastage of food items with the help of a Random Forest-based machine learning model along with contextual parameters of food type, quantity, event category, storage conditions and pricing details. The backend of this system depends on FastAPI and uses secure role-based authentication using JWT. The frontend is designed on React algorithm and includes specific roles in dashboards for restaurants, NGOs and administrators to access. Automatic email notifications support involvement of stakeholders for geographic connection increases their contribution using map-driven decision-making, distance computation reduces logistical delays and improving efficiency. Overall, the system demonstrates the AI and GIS technology can be efficiently combined to generate a low-cost, scalable and long-term solutions for reducing food waste and improving shared meals.

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