Site-specific performance analysis of wind turbines is crucial but computationally prohibitive due to the high cost of evaluating numerical models. To address this, the authors propose a machine learning model combined with dimensionality reduction using principal component analysis and the discrete cosine transform, along with a long short-term memory model, to predict dynamic responses at a fraction of the computational cost.