Aiming at the fault problem of wind turbine gearbox overtemperature, a fault early warning model of wind turbine gearbox based on improved parameter optimization machine learning algorithm is proposed. Firstly, the characteristic variables are determined by random out-of-the-pocket estimation, and the input variables are filtered by sliding smoothing filtering. Secondly, the gray wolf algorithm optimization support vector regression model is constructed, and the state identification index is determined according to the residual value of the optimal model output. Finally, the threshold range is set by the time-lapse sliding window, and the alarm is immediately issued when the status identification indicator exceeds the threshold range. Experimental results show that the model can issue a fault warning for the temperature abnormality of the wind turbine gearbox 87 minutes in advance, and the early warning effect is better than that of the distance correlation coefficient-GWO-SVR model, Pearson-GWO-SVR model and OOB-SVR model.