The rapid integration of Artificial Intelligence (AI) technologies into supply chain management (SCM) represents a paradigm shift altering traditional logistics and production infrastructures across industries. This paper presents an exhaustive theoretical analysis of how AI transforms supply chain operations, underscoring opportunities, challenges, and emergent research avenues. Drawing on extensive literature — covering general AI applications in industries (Rashid & Kausik, 2024; Khaleel, Jebrel & Shwehdy, 2024), focused examinations of AI in supply chains (Sharma et al., 2022; Pournader et al., 2021; Sharma, Gunasekaran & Subramanian, 2024), and foundational SCM theory and logistics frameworks (Cooper, Lambert & Pagh, 1997; Christopher, 2016; Chopra & Meindl, 2019) — the analysis begins by situating supply chains in their traditional conceptualization and proceeds to map the transformative influence of AI across procurement, production, inventory management, logistics, and demand forecasting. Methodologically, this study conducts a conceptual synthesis and critical discourse analysis of extant literature, identifying thematic patterns, theoretical propositions, and gaps. The results highlight that AI enables predictive analytics, real-time decision-making, and network-wide optimization, while simultaneously introducing ethical, data governance, and implementation complexity concerns. In the discussion, theoretical implications are explored, including the redefinition of supply chain boundaries, the evolving role of human agents, and systemic resilience in the face of disruptions, along with limitations of existing literature. The paper concludes by proposing a comprehensive research agenda to guide future empirical and normative inquiry into AI-driven SCM transformations.