Artificial Intelligence and Optimization in Food Waste Management: A Systematic Review of Sustainability and Profitability

Abstract

Food waste represents a critical structural challenge to the sustainability and profitability of the global food service industry. This study presents a Systematic Literature Review (SLR) under the PRISMA protocol, analyzing 59 high-impact articles indexed in Scopus and Web of Science during the period 2023–2026. The objective was to evaluate the effectiveness of predictive and optimization technologies in mitigating waste and improving operational efficiency. The results reveal a technological dichotomy: while optimization models predominate (54.55%) for resource planning, machine learning (40.00%) is established as the superior tool for demand forecasting in volatile environments. However, a critical implementation gap was identified: 59.32% of the studies validate their models exclusively using mathematical error metrics (RMSE, MAPE), while only 25.42% report tangible operational KPIs such as return on investment or volumetric waste reduction. It is concluded that, in order to move from theoretical precision to industrial utility, future research must integrate economic loss functions into algorithmic training.