Abstract In this talk, three machine learning (ML) algorithms viz. Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGBoost) are implemented to predict wake velocity and turbulence intensity from a wind turbine at different downstream distances. To this end, a set of high-fidelity numerical simulations are performed for the NREL Phase VI wind turbine to produce training and test datasets for the three machine learning algorithms. Using the trained model, the wake flow field downstream of the blade and turbulence intensity are predicted on the test datasets which are hidden from the trained model...