Journal of Scientific Research and Reports

Sustainable supply chain management has become increasingly important as organisations respond to geopolitical disruption, climate-related risk, regulatory pressure and rising stakeholder expectations. This systematic review examines how artificial intelligence (AI) enables sustainable supply chain management by strengthening resilience, traceability, transparency and environmental, social and governance (ESG) performance across supply networks. The review synthesises peer-reviewed literature published up to 2025 and is structured in accordance with the PRISMA 2020 guidelines. Drawing on Supply Chain Resilience Theory, Dynamic Capabilities Theory, Information Processing Theory, Transaction Cost Economics, Stakeholder Theory and the Triple Bottom Line framework, the paper integrates empirical, conceptual and simulation-based evidence. The findings show that AI-enabled predictive analytics, machine learning, digital twins and optimisation models can support anticipatory and adaptive resilience by improving demand forecasting, risk identification, scenario simulation and recovery planning. The review also indicates that AI-supported traceability systems, particularly when integrated with blockchain and the Internet of Things, can enhance end-to-end transparency, ethical sourcing and compliance oversight in complex, multi-tier supply chains. Evidence on ESG performance suggests that AI adoption is most consistently associated with environmental improvements, including emissions reduction, energy efficiency and resource optimisation, while social and governance outcomes remain less consistently measured and comparatively under-researched. Overall, the review shows that AI functions as an enabling infrastructure rather than a deterministic solution for sustainability. Its contribution depends on organisational readiness, data governance, technological convergence, inter-organisational collaboration and institutional alignment. The paper contributes an integrated synthesis of AI-enabled sustainable supply chain management and identifies mechanisms, limitations and research gaps relevant to managers, policymakers and scholars.