This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) often fall short in capturing complex environmental interactions, leading to inaccurate soil loss predictions. This research introduces a novel approach using Convolutional Neural Networks (CNNs) combined with Geographic Information Systems (GISs) to improve the precision and spatial resolution of soil loss risk assessments. High-resolution satellite imagery, soil maps, and climatic data were processed to extract critical factors, such as slope, land cover, and rainfall erosivity, which were then fed into the CNN model. The findings revealed that the CNN model outperformed traditional methods, achieving a low Root Mean Square Error (RMSE) of 2.3 and an R-squared value of 0.92, significantly surpassing the USLE and RUSLE models. The resulting high-resolution soil loss maps identified high-risk erosion areas, particularly in the central and eastern regions of the watershed, with soil loss rates exceeding 40 tons/ha/year. These findings demonstrate the superior predictive capabilities of deep learning, offering valuable insights for targeted soil conservation strategies and highlighting the potential of advanced computational techniques to revolutionize environmental modeling.