The issue of sustainability has become a severe issue in supply chain management due to the significant role coordination and transportation play in global greenhouse emissions (GHG). Data-based approaches incorporating big data analytics, machine learning (ML), and artificial intelligence (AI) have significant potential for monitoring, assessing, and minimising the carbon footprint in supply chains. This study is a methodological literature review of 979 articles indexed by Scopus, mapping the intellectual landscape of this new field. Using a multi-tool bibliometric approach, this study also searched publication patterns, central outlets of research, thematic clusters, and international collaboration networks (R Biblioshiny, VOSviewer, Python). The findings demonstrate that the current research environment is defined by a two-fold focus on computational intelligence and environmental sustainability methods. Although considerable advances have been made in the use of analytics in discrete problems, such as route optimisation, there is an apparent gap in the creation of end-to-end systems to address comprehensive carbon management. The study also shows that the activity is concentrated among a select group of core journals and top research countries; thus, there is a need to make global collaboration more extensive and all-inclusive. The final conclusions support the implementation of AI-enabled models that will complement the goals of efficiency, resilience, and sustainability throughout the entire supply chain, thus outlining key gaps in research and future opportunities.