Authors : Aditi Guajr; Vaishnavi Bagmar; Karina Jain; Saniya Dhopavkar; Mahesh Pawaskar

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/4f4r4pxu

Scribd : https://tinyurl.com/2p9jd2cv

DOI : https://doi.org/10.38124/ijisrt/26apr1440

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Food waste remains one of the most pressing global sustainability challenges, significantly contributing to environmental degradation, economic loss, and food insecurity. A major cause of household and small-scale commercial food waste is inefficient inventory management and the absence of intelligent tracking systems capable of monitoring freshness, expiry timelines, and nutritional relevance. To address these challenges, this paper presents EatWise, an AIdriven smart food management application designed to minimize food waste while simultaneously promoting healthier eating habits. EatWise integrates barcode scanning technology, manual food logging, machine learning–based expiry prediction models, and personalized nutritional guidance into a unified platform. The system prioritizes food consumption based on predicted expiry urgency and provides tailored dietary recommendations aligned with user-defined health objectives such as weight loss, weight gain, or balanced nutrition. By combining real-time alerts with sustainable food utilization strategies.

Keywords : Food Waste Reduction; Artificial Intelligence (AI); Expiry Prediction; Smart Inventory Management; Nutrition Optimization; Barcode Scanning; Machine Learning; Sustainable Consumption.

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