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The abstract describes a comparative study of various machine learning techniques for wind turbine performance prediction. The dataset used in this study is obtained from the National Renewable Energy Laboratory (NREL) and contains meteorological data and power output from a wind turbine. The machine learning techniques considered in this study include artificial neural networks (ANN), decision trees (DT), and random forests (RF). The results show that RF outperforms ANN and DT in terms of RMSE and MAE, while ANN outperforms DT and RF in terms of R-squared. Overall, this research demonstrates the effectiveness of machine learning techniques for wind turbine performance prediction and provides insights on the advantages and disadvantages of certain machine learning approaches. The findings of this research can be used to guide wind farm managers in selecting appropriate machine learning techniques for wind turbine performance prediction.