This study explores the role of Artificial Intelligence (AI) in advancing sustainability across global supply chains (SC), supply chain management (SCM), and sustainable supply chains (SSC), with a focus on the Triple Bottom Line (TBL) dimensions: economic, environmental, and social. Using a systematic literature review guided by the PRISMA framework, the analysis includes 111 peer-reviewed articles from Scopus, Web of Science, and ProQuest. Findings show that most research emphasizes AI’s benefits for environmental and economic outcomes, such as emissions reduction, operational efficiency, and predictive planning, while largely neglecting its implications for social sustainability. The review identifies key sectoral applications in agriculture, logistics, urban infrastructure, and apparel, and highlights the potential of AI, particularly when combined with IoT, blockchain, and big data analytics, to improve traceability and circularity. However, critical research gaps persist, including algorithmic opacity, underexplored trade-offs across TBL pillars, limited attention to managerial authority shifts, and insufficient focus on developing economies. These challenges raise concerns about fairness, ethical governance, and organizational blind spots. Grounded in the principles of Industry 5.0 and aligned with the UN Sustainable Development Goals (SDGs), the study calls for more reflexive, ethically informed, and context-sensitive approaches to AI adoption in SSCM.

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Sustainable supply chain management (SSCM),Artificial Intelligence (AI),Supply Chain (SC),Sustainable Development Goals (SDGs),Industry 5.0 (I5.0)

• Economics and Business - Social Sciences
• Other Social Sciences - Social Sciences