Monitoring and predicting critical thermal conditions in wind turbines presents significant challenges due to operational variability and the complexity of thermodynamic interactions. In this study, an innovative approach that integrates statistical analysis with artificial intelligence techniques for predicting critical temperatures in Goldwind S50/750 wind turbines, using historical SCADA system data through a hybrid methodology that statistically characterizes thermal variables and subsequently applies convolutional neural networks (CNN) to develop predictive models capable of capturing nonlinear relationships and multivariate temporal dependencies. Statistical analysis reveals positively skewed distributions with extreme values exceeding 140 ℃, identifying the generator winding as the most critical component (25.5% variability), while the developed CNN model achieved exceptional performance with a coefficient of determination (R2) of 0.94, an RMSE of 1.73 ℃, and a mean absolute error (MAE) of 1.25 ℃ for winding temperature prediction. The significance of these results lies in their ability to shift maintenance strategies from reactive to predictive, enabling early identification of critical thermal conditions and integration into real-time intelligent monitoring systems, thus contributing to the development of more efficient and reliable wind farms, particularly relevant for tropical regions where thermal management poses a greater operational challenge.