International Journal of Science and Technology (IJST)

AI-Powered Sustainable Supply Chains: A Machine Learning Framework for Circular Economy Transitions by 2040

Integration of artificial-intelligence (AI) and machine-learning (ML) technologies within sustainable supply chain management (SSCM) represents a paradigm shift toward achieving circular economy (CE) objectives by 2040. This systematic literature review examines the convergence of AI-powered systems and circular supply chains through a comprehensive analysis of 170 peer-reviewed articles from Q1 Scopus-indexed journals published between 2020-2025. The study identifies critical research gaps in AI-driven circular economy transitions and proposes a novel dual-framework approach for implementing intelligent sustainable supply chains. Our findings reveal that while AI applications in supply chain optimization have increased by 300% since 2020, only 23% of current implementations specifically target circular economy principles. The research contributes by developing two innovative frameworks: (1) the AI-Circular Economy Integration Model (AI-CEIM) and (2) the Machine Learning Sustainability Assessment Framework (ML-SAF). Through structural equation modeling (SEM) analysis of 12 organizational case studies, we demonstrate that AI-powered circular supply chains can achieve 45% reduction in waste generation, 38% improvement in resource efficiency, and 52% enhancement in supply chain resilience by 2040. The study provides actionable insights for practitioners and establishes a roadmap for future research in AI-driven sustainable supply chain management.