With the rapid expansion of wind energy generation, ensuring the efficient operation and maintenance of wind turbines has become paramount. This paper puts forward a comprehensive approach for fault detection in wind turbines leveraging the Internet of Things (IoT) technology. The integration of IoT ensures real-time monitoring and data collection from different sensors installed on wind turbines, including infrared sensors, temperature sensors. This data is transmitted to a centralized monitoring system where advanced analytical techniques such as machine learning algorithms are employed for fault detection. The proposed approach offers several advantages over traditional methods, including early detection of faults, predictive maintenance scheduling, and reduced downtime. Furthermore, by utilizing IoT technology, remote monitoring and diagnostics can be performed, allowing for timely intervention and optimization of maintenance resources.The effectiveness and efficiency of our proposed approach is demonstrated through case studies and simulations, highlighting its capability to accurately detect various types of faults in wind turbines. Overall, this research contributes to enhancing the reliability and performance of wind energy systems, ultimately facilitating the transition towards a more sustainable energy future.