Abstract Artificial Intelligence (AI) is increasingly transforming manufacturing supply chains by enabling intelligent decision-making, adaptive operations, and more efficient resource utilization. Its potential to advance sustainable operations management, however, extends beyond technological adoption to the organizational integration of AI capabilities into operational processes and sustainability-oriented strategies. Despite growing scholarly attention, research in this field remains fragmented across technological applications, manufacturing functions, organizational conditions, and sustainability dimensions. In particular, limited conceptual integration exists regarding how AI capabilities are translated into operational transformation and under what organizational and technological conditions such transformation generates sustainable value. To address these limitations, this study conducts a systematic literature review and structured research gap analysis of peer-reviewed studies published between 2016 and 1 June 2026. The review examines the intellectual evolution of the field, synthesizes dominant research themes, evaluates AI applications across manufacturing and supply chain operations, and critically assesses their implications for operational, environmental, economic, and social sustainability. The synthesis highlights an emerging shift from isolated, function-specific AI applications towards interconnected and increasingly autonomous operational ecosystems, supported by the convergence of AI with the Internet of Things (IoT), digital twins, cyber-physical systems, and advanced analytics. These developments create opportunities for predictive and prescriptive decision-making, intelligent production planning, predictive maintenance, quality optimization, resource efficiency, supply chain visibility, and disruption management. Nevertheless, the realization of these benefits remains contingent upon organizational readiness, digital infrastructure maturity, data quality, workforce capabilities, effective governance, and the alignment of technological investments with sustainability priorities. The review further identifies persistent theoretical, methodological, technological, organizational, and empirical gaps, particularly concerning human–AI collaboration, implementation scalability, algorithmic accountability, sustainability trade-offs, and the assessment of long-term performance outcomes. Building on these insights, the study develops an AI-Driven Sustainable Operations Management Strategic Framework that integrates AI technological capabilities, organizational and digital enablers, AI-enabled operational transformation mechanisms, sustainability-oriented governance, and multidimensional performance outcomes. By connecting technological potential with organizational transformation and sustainability performance, the proposed framework provides an integrative conceptual foundation for understanding AI-enabled sustainable value creation across manufacturing supply chains. It also establishes a structured agenda for future research and offers strategic implications for manufacturing organizations and policymakers seeking to advance responsible AI adoption, strengthen operational resilience, and achieve sustainable long-term competitiveness.