Adaptive aerostructures, such as morphing blades, provide a crucial improvement to the control of a wind turbine beyond what is possible with pitch and torque control. However, using these new axes of control proves difficult due to computational expense in real-time applications. In this paper, the DTU 10 MW reference turbine is used to create data-driven models for a variety of load-related turbine performance parameters on a turbine with twist morphing capability. Data is gathered using OpenFAST to simulate the twist morphing under steady-state conditions. This data is then used to train decision trees, Support Vector Machines (SVMs), Neural Networks (NNs), and Gaussian Process Regression (GPR) data-driven models for each target performance parameter. The models are then compared in terms of accuracy and prediction speed to evaluate which model is most suitable for the application. In all examined cases, the models created with GPR were optimal in terms of accuracy, with reductions in Root-Mean-Square Error (RMSE) of between 61 and 86% as compared to the second-best performing model in each case. Prediction speed for these GPR models were at least comparable to competing models in all cases, and in one case was the fastest model examined. Overall, GPR performed the best of the examined modeling techniques for all cases, indicating its suitability for model loading in this application in future work.