Investigating the potential for wind-induced soil erosion in arid and semi-arid regions is essential for understanding soil degradation and its associated impacts, such as agricultural productivity reduction, infrastructure damage, air quality decline, and adverse health effects. This study pioneers the integration of remote sensing data and Artificial Neural Networks (ANN) for wind erosion mapping, offering a novel approach to analyzing soil surface dynamics. ANN models were implemented to estimate aerodynamic roughness (z0) and friction velocity (u*) using Sentinel-1 Synthetic Aperture Radar (SAR) data. These estimates were further integrated with meteorological datasets to identify areas prone to wind erosion and, subsequently, dust storms. The results indicated that wetlands, with the highest z0 (6.98 cm) and u* (0.81 m/s) values have a negligible potential for wind erosion. Conversely, clay flats showed the lowest values (z0 = 0.89 cm, u* = 0.42 m/s), suggesting a higher susceptibility to wind erosion. Finally, the developed model was applied to generate wind erosion potential maps of the study area, serving as a practical asset for the identification of high-risk zones prone to erosion. This study emphasizes the importance of soil surface parameters to identify potential areas of wind erosion for developing more accurate dust emission models, which support effective management of wind erosion and mitigate the adverse effects of this environmental phenomenon. Although regionally focused, the methodology is transferable to other arid and semi-arid environments, offering valuable insights for soil conservation and land management.