This study presents a thorough examination of artificial intelligence (AI) and machine learning (ML) applications in the control systems of grid-connected wind turbines inside cyber-physical systems (CPS). Renewable energy sources, particularly wind energy, play an important role in modern power grids; therefore, effective control systems are required to ensure stability, reliability, and performance. Traditional control approaches frequently struggle to cope with wind energy variability and grid demand dynamics. AI and machine learning provide unique solutions by allowing for better real-time control, predictive maintenance, and wind turbine performance improvement. The review begins by discussing the challenges of wind turbine control, such as variable wind conditions, grid integration issues, and maintaining optimal power generation. It then investigates the application of AI and machine learning techniques like as neural networks, reinforcement learning, and fuzzy logic to improve control precision, grid stability, and overall system efficiency. CPS is also mentioned in terms of providing a strong framework that connects physical wind turbine components with digital control systems for enhanced monitoring and decision-making. Case studies and current breakthroughs are examined to demonstrate the efficacy of AI and ML-driven techniques in real-world wind energy applications. The report continues by outlining critical areas for future research, highlighting the importance of further integrating AI technologies with CPS to increase the resilience and sustainability of grid-connected wind turbines. This review seeks to help researchers and practitioners create cutting-edge solutions for modern wind energy systems.