Çali, SedefToy, Ayhan ÖzgürEkren, Banu Yetkin2026-04-072026-04-0720252405-89712405-896310.1016/j.ifacol.2025.09.0442-s2.0-105018802808https://hdl.handle.net/123456789/14797https://doi.org/10.1016/j.ifacol.2025.09.044Agri-food Supply Chains (AFSCs) face increasing disruptions from natural disasters, pandemics, and economic crises, necessitating robust quantitative analysis for resilience. This study conducts a Systematic Literature Review (SLR) using text mining and Latent Dirichlet Allocation (LDA) to identify six key research themes, including risk management, pandemic effects, simulation-based resilience, climate change, market price volatility, and optimization models. Findings reveal that multi-criteria decision-making, simulation, optimization, and machine learning are widely used, yet gaps remain in Artificial Intelligence (AI)-driven risk prediction, real-time data integration, and adaptive decision-making This review offers insights for researchers and practitioners, emphasizing the need for AI, digital twins, and blockchain to enhance AFSC resilience. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)eninfo:eu-repo/semantics/openAccessText MiningLiterature ReviewAgri-Food Supply ChainResilient Supply ChainsLatent Dirichlet AllocationQuantitative MethodsConference Object