The increased integration of wind energy into the power system network requires advanced testing and performance evaluation methods to ensure reliability and efficiency. This paper presents an IoT-based real-time data collection method for analyzing the performance of the Wind Power Generation System (WPGS) using an intelligent IoT-enabled wind emulator system. The proposed system uses IoT to gather comprehensive real-time wind data, which is processed by a digitally controlled emulator to accurately assess wind turbine responses under different wind conditions. The advantage of this system is that IoT and cloud-based data analytics enable predictive analysis of the WPGS, helping evaluate its behavior and performance under various real-time scenarios. The approach is tested with a wind emulator setup consisting of a 1 kW Doubly-Fed Induction Generator (DFIG) connected to a Brushless DC (BLDC) motor, where the wind turbine model is developed on the VEE Pro platform, integrating an IoT-NodeRed, cloud API, and FPGA controller to simulate real-world wind conditions. Unlike conventional systems, the proposed architecture achieves real-time synchronization between global weather data and emulator control with low latency of 180 ms. Experimental results indicate 87% model accuracy Mean Absolute Percentage Error (MAPE) between theoretical and emulator power outputs, 95% health index reliability, and near-unity grid power factor of 0.999. This study provides a cost-effective, scalable, and adaptable solution for real-time wind energy analysis, supporting ongoing research and development.