We propose in this paper a high-temperature non-linear modeling for the I-V characteristics of GaN150 HEMT. Three different data-driven models were developed for a temperature range varying from 25°C to 250°C, by using three machine learning regression techniques namely: The Artificial Neural Network (ANN), the Support Vector Machine (SVM) and the Decision Tree (DT). Experiments were conducted on a GaN150 device with a width of 40 μm and accordingly, a set of measurements were obtained and exploited to build the device model. The three models were evaluated based on their ability to predict the I-V characteristics outside the temperature range (greater than 250°C) and their mean square error. The obtained results show that the models predict the device characteristics correctly based on the calculated mean squared error between the actual and predicted characteristics.