Abstract: Breakthrough advancements in artificial intelligence provide disruptive technological support for building smart power plant models with features such as self-optimization, self-adaptation, and self-maintenance, driving a profound transformation of the power industry from traditional automation to genuine intelligence. A systematic review is conducted on the latest progress in the deep integration of AI technologies with power plant production processes, focusing on their core role in enhancing plant efficiency, safety, and reliability. By establishing a tripartite analytical framework of “operation-inspection-maintenance”, an in-depth evaluation is carried out on the application of typical algorithms in scenarios such as thermal system operation optimization, prediction of complex operating condition parameters, and life-cycle fault diagnosis and early warning of equipment. Furthermore, several key bottlenecks are identified that currently hinder the large-scale application of AI technology in electric power production systems: firstly, the lack of model interpretability renders algorithmic decision-making processes akin to a “black-box”, making it difficult to gain full trust from operators and impeding adoption in safety-critical power systems; secondly, faced with complex and variable on-site operating conditions, existing models often rely on large amounts of labeled data and exhibit weak generalization capability under scenarios such as condition shifts or fuel variations; additionally, challenges remain in integrating AI models with existing industrial control hardware and real-time operating systems, while the computational power and energy consumption constraints of edge computing devices also limit the on-site deployment of complex models. To effectively enhance the intelligence level of power plants and overcome these bottlenecks, a “cloud-edge-end” collaborative industrial field solution and a gradual technological evolution path are proposed, incorporating cutting-edge AI technologies, domain-specific large-scale models for the power sector, and industrial internet architecture. This system aims to provide systematic practical guidance and technical support for thermal power plants in achieving safe, efficient, clean, and low-carbon intelligent transformation and upgrading, thereby assisting the energy and power industry in realizing high-quality development in the digital era.