The oil and gas industry are the main driver of the world's greenhouse gas emissions and countermeasures are required to achieve international targets for reducing emissions. Carbon Capture, Utilization, and Storage technologies have become leading emission-reducing means but are constrained by high cost of operations, system complexity, and long-duration reliability. This review paper discusses the application of Artificial Iintelligence to Carbon Capture, Utilization, and Storage systems as one of the main pathways to enhancing environmental sustainability for the oil and gas industry. The review discusses recent advances in Artificial Intelligence, including deep learning, reinforcement learning, and machine learning, including means by which they are being applied to CCUS value chain, including CO₂ capture, transportation, storage, and utilization. This paper examines the roles played by AI to maximize capture process, pipeline leak prevention, capture process monitoring accuracy, and CO₂ utilization by enhanced oil recovery (EOR). It considers constraints and bottlenecks to AI applications including lack of data, models lack of interpretability, cybersecurity vulnerabilities, and limitations to interdisciplinary cooperation. The review concludes by asserting that despite limitations, AI holds revolutionary potential to make CCUS efficient, cost-sensitive, and scalable to position it as key enabler of industry transition to the era of low-carbon production.