This review explores the role of artificial intelligence (AI) in enhancing power system stability, control, and protection. The paper discusses the application of AI techniques such as deep learning, machine learning, fuzzy logic, reinforcement learning, and model predictive control to address challenges in modern power systems. It emphasizes the importance of power system stability and the integration of diverse energy sources. The review highlights the effectiveness of AI in predictive maintenance, fault detection, real-time control, and monitoring. It also explores the potential of AI in decision-making under uncertainty, dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering insights into practical applications. The review identifies current limitations and suggests areas for future research, emphasizing the need for more robust, flexible, and scalable intelligent systems in the power sector. The paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection. Keywords: smart grid; artificial intelligence; power system stability; power system protection; wavelet transformation; neural network; evolutionary algorithms.This review explores the role of artificial intelligence (AI) in enhancing power system stability, control, and protection. The paper discusses the application of AI techniques such as deep learning, machine learning, fuzzy logic, reinforcement learning, and model predictive control to address challenges in modern power systems. It emphasizes the importance of power system stability and the integration of diverse energy sources. The review highlights the effectiveness of AI in predictive maintenance, fault detection, real-time control, and monitoring. It also explores the potential of AI in decision-making under uncertainty, dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering insights into practical applications. The review identifies current limitations and suggests areas for future research, emphasizing the need for more robust, flexible, and scalable intelligent systems in the power sector. The paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection. Keywords: smart grid; artificial intelligence; power system stability; power system protection; wavelet transformation; neural network; evolutionary algorithms.