Machine Learning in Carbon Capture, Utilization, Storage, and Transportation: A Review of Applications in Greenhouse Gas Emissions Reduction

Carbon Capture, Utilization, and Storage (CCUS) technologies have emerged as indispensable tools in reducing greenhouse gas (GHG) emissions and combating climate change. However, the optimization and scalability of CCUS processes face significant technical and economic challenges that hinder their widespread implementation. Machine Learning (ML) offers innovative solutions by providing faster, more accurate alternatives to traditional methods across the CCUS value chain. Despite the growing body of research in this field, the applications of ML in CCUS remain fragmented, lacking a cohesive synthesis that bridges these advancements to practical implementation. This review addresses this gap by systematically evaluating ML applications across all major CCUS components—CO2 capture, transport, storage, and utilization. We provide structured representative examples for each CCUS category and critically examine various ML techniques, optimization objectives, and methodological frameworks employed in recent studies. Additionally, we identify key parameters, practical limitations, and future opportunities for applying ML to enhance CCUS systems. Our review thus offers comprehensive insights and practical guidance to CCUS stakeholders, supporting informed decision-making and accelerating ML-driven CCUS commercialization.