This study explores the development of an Artificial General Intelligence (AGI) enabled framework aimed at reducing CO2 emissions globally over the supply chain. While traditional Artificial Intelligence (AI) has been instrumental in addressing inefficiencies and optimizing supply chain processes, its dependency on predefined parameters and static environments limits its adaptability. The AGI framework provides a transformative solution by autonomously learning, reasoning, and generalizing strategies across diverse supply chain scenarios, enabling dynamic decision-making and real-time optimization without human intervention. The proposed framework integrates AGI into supply chain management to enhance operational efficiency and align with sustainability goals. By leveraging AGI for clustering, dynamic routing, the study demonstrates how carbon footprints can be significantly reduced across supply chain nodes. Illustrative examples highlight AGI’s ability to identify inefficiencies, adapt to changing conditions, and foster sustainable practices, creating a flexible and scalable framework adaptable to various industries.