Saharan sand encroachment on roads poses severe hazards, disrupting transportation and increasing the risk of accidents in desert regions. This study explores the application of deep learning models for the detection and segmentation of sand deposits on road surfaces using satellite imagery and road camera data. We compare the performance of four prominent models: Convolutional Neural Networks (CNN), U-net, ResNet50, and SqueezeNet. The U-net model is particularly effective in achieving high-resolution segmentation, accurately delineating sand-covered areas, while ResNet50 excels in feature extraction due to its deep architecture. CNN provides a solid baseline for sand detection, and SqueezeNet offers a lightweight alternative suitable for deployment on resource-constrained devices. Through extensive experiments, we demonstrate the strengths and limitations of each model, ultimately providing a comprehensive solution for the real-time monitoring and management of Saharan sand on roads. Our findings suggest that integrating these models into a cohesive system can significantly enhance road safety and maintenance strategies in desert environments.