A Comprehensive Review of Leveraging Artificial Intelligence for CO 2 Emission Reduction Through Enhanced CCUS Design and Operation

ABSTRACT The urgent need to decarbonize energy systems has heightened global focus on Carbon Capture, Utilization, and Storage (CCUS) technologies. This focus is particularly acute within OPEC countries, where hydrocarbon‐dependent economies face the dual challenge of reducing CO 2 emissions while preserving economic stability and global energy market relevance. Artificial Intelligence (AI) emerges as a transformative enabler, offering advanced methodologies—including machine learning, deep learning, reinforcement learning, Gaussian process regression, and fuzzy logic modelling—to optimize CCUS systems across multiple dimensions. This review systematically evaluates state‐of‐the‐art AI applications that improve CO 2 capture efficiency, optimize materials, predict storage behaviour, and enable real‐time leak detection. Furthermore, AI facilitates seamless integration of CCUS with renewable energy sources, supports intelligent decision‐making for utilization pathways such as enhanced oil recovery and synthetic fuel production, and advances techno‐economic optimization. Through critical analysis of recent studies, the review underscores AI's role in reducing the cost, uncertainty, and environmental impact of large‐scale CCUS deployment in hydrocarbon‐dependent economies, including OPEC member nations. Despite challenges related to data availability, computational demands, and model transparency, AI's potential to accelerate a low‐carbon energy transition remains compelling. The study recommends strategic policy and research priorities to promote AI‐driven innovation and sustainable climate action in hydrocarbon‐dependent economies.