As a key branch of emerging energy storage technologies

underground compressed air energy storage (CAES) is gaining increasing attention for its advantages in large-scale capacity

long-duration storage

and environmental sustainability

making it a crucial support for new power systems. However

underground CAES projects often face challenges such as complex geological conditions

significant multi-physical field coupling

and frequent injection-production cycles

where traditional methods show clear limitations in modeling accuracy and operational efficiency. In recent years

artificial intelligence (AI) technologies

with their powerful nonlinear modeling and data-driven capabilities

have offered novel approaches to intelligent site selection

structural prediction

system operation

and risk warning in underground CAES. This paper employs bibliometric analysis and knowledge mapping techniques to systematically review the current state of AI applications in underground CAES

covering typical scenarios such as site selection and geological modeling

intelligent cavern construction

stability prediction

injection-production optimization

multiphysical coupling modeling

and safety monitoring. The findings reveal that research in this field remains in its early stages

lacking a comprehensive and systematic framework. Based on existing studies

this paper proposes several key directions for future development

including physics-informed modeling

multi-source data integration

and the construction of intelligent engineering platforms

aiming to provide theoretical insights and technical references for advancing the intelligent development of underground CAES and supporting the realization of China’s dual carbon goals.