The assessment of wind energy resources in large regions presents a substantial challenge for energy system operators due to the intricacies involved in accurately describing all atmospheric circulation patterns and, even more so, modeling their inherent dynamic. This research primarily aimed to identify the prevailing synoptic weather patterns over Mexico and surrounding regions, thereby enabling the assessment of their influence on the wind field and energy production in Mexico. This study employed ERA5-reanalysis daily mean sea level pressure anomalies data to classify large-scale weather patterns, and two widely recognized clustering techniques, $K$-means and self-organized maps, were utilized for this purpose. Additionally, the study developed a probabilistic approach to establish a regional wind-speed model that facilitates the estimation of factors associated with the potential wind energy production and their relationships with the fluctuations in large-scale weather patterns over the Mexican region. The research findings underscore the crucial influence of these weather patterns on the energy production and emphasize the necessity of a comprehensive study within a forecasting framework to enhance the current Mexican electrical system’s operation.