Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3661 2018 2018 Faculty of Artificial Intelligence, Hours University, Egypt; Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura, 35516, Egypt Mona Mona School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, Jordan El-Sayed M. El El-kenawy Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura, 35516, Egypt Mohamed Gamal Abdel Abdel-Fattah Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura, 35516, Egypt Islam Ismael Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura, 35516, Egypt Hossam El.Deen Salah Mostafa Wind energy is one of the fastest-growing sustainable, clean, and renewable sources, attracting significant attention and investment from many countries. However, given the substantial capital investment required for wind power plants, understanding the proposed plants’ performance becomes critical before implementation. This assessment is most effectively conducted using refined wind power predictability models and precise wind velocity data. Accurate wind forecasts are essential for informed decision-making and effective wind energy utilization. In this study, three advanced Machine Learning (ML) regression methods were applied to the TNWind dataset to predict the power output of wind turbines. The dataset variables included date and time (measured at 10-minute intervals), low-voltage active power (in kW), wind speed (in ms), the theoretical wind power curve (in kWh), and wind direction. To predict wind power output, six supervised ML models were trained, including Random Forest Regressor (RF), Extreme Gradient Boosting Regressor (XGB), Gradient Boosting Regressor (GB), Support Vector Machine Regressor (SVR), K-Neighbors Regressor (KN), and Linear Regressor. The analysis revealed that the Random Forest model outperformed the others, achieving exceptional performance metrics: an R2 value of 0.97, an MAE of 0.17 and an MSE of 0.07. The analysis to identify the outcomes for wind power generation from machine learning proves that renewable energies are more capable and are a lucrative investment. 2025 2025 64 81 10.54216/FPA.190205 https://www.americaspg.com/articleinfo/3/show/3661