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Agri-food supply chains are increasingly exposed to overlapping disruptions arising from climate variability, geopolitical instability, pandemic effects, price volatility, labour shortages, and changing consumer demand. These pressures have intensified interest in resilience-oriented technologies that can help supply chains anticipate disruption, absorb shocks, adapt operations, and recover service continuity. Artificial intelligence has emerged as a prominent candidate because it can transform fragmented supply chain data into forecasts, alerts, recommendations, and operational decisions. This systematic review examines how artificial intelligence is being applied to strengthen agri-food supply chain resilience. The review focuses on four functional domains: forecasting, traceability, risk detection, and decision support. It asks whether current AI applications merely improve isolated technical tasks or whether they provide demonstrable evidence of resilience improvement across agri-food supply systems. The review follows a systematic design informed by PRISMA reporting principles and structured literature review procedures. A corpus of 32 peer-reviewed journal articles published between 2017 and 2025 was synthesised. The selected studies cover AI forecasting models, blockchain-enabled traceability, quality and safety monitoring, logistics optimisation, supply chain decision support, and review-methodology guidance. The synthesis shows that forecasting research is technically advanced, especially in price prediction, demand forecasting, and quality-sensitive perishable supply chain management. Traceability and risk detection studies show strong growth around blockchain, IoT, big data, computer vision, and food safety monitoring. Decision support research connects AI to resilience more directly, but most evidence remains conceptual, model-based, or function-specific rather than validated through real disruption events. The review concludes that AI has substantial potential to support agri-food supply chain resilience, but the evidence base remains fragmented. Existing studies tend to focus on single functions such as forecasting accuracy, provenance visibility, or routing efficiency, while system-level resilience outcomes are rarely measured. Future research should therefore move from isolated AI applications toward integrated, empirically validated, resilience-focused agri-food intelligence systems.

Smart farming technologies are frequently associated with improved efficiency, environmental performance, and adaptive agricultural management, yet digital capability alone does not establish sustainability. This article proposes a sustainability-by-design framework that embeds environmental, economic, social, resilience, and governance considerations into the design and evaluation of smart farming technologies. The framework connects digital agriculture capabilities with resource-use baselines, water- and nutrient-use assessment, conditional pesticide and energy considerations, soil and biodiversity relevance, waste and emissions evaluation, and human oversight. It further integrates climate-risk assessment, scenario planning, adaptive farm-system design, operational resilience, learning feedback, and explicit boundaries against treating monitoring or prediction as proof of climate resilience. Social value is addressed through farmer participation, autonomy, digital inclusion, accessibility, usability, affordability, local relevance, knowledge integration, trust, transparency, data governance, and accountability. Cross-cutting sustainability design principles include context sensitivity, life-cycle thinking, interoperability, trade-off assessment, uncertainty communication, evaluation planning, controlled updating, and clearly defined decision boundaries. The proposed framework is intended to organize design choices, evidence requirements, governance safeguards, and future evaluation rather than prescribe farm-management actions or certify sustainable outcomes. Its main contribution is a structured socio-technical model for examining when, how, for whom, and under what conditions smart farming technologies may support sustainability-oriented agricultural transformation.