Potholes are a major concern for road infrastructure, traffic safety, and vehicle maintenance. Manual inspection methods for pothole detection are labor-intensive, time-consuming, and often inefficient for large road networks. This study evaluates and compares the performance of YOLOv5 and Single Shot Detector (SSD) models for automated pothole detection under diverse weather and lighting conditions. Using the Multi-Weather-Based Dataset (MWBD), images captured during daytime, twilight, and nighttime were annotated with bounding boxes and enhanced through data augmentation techniques such as shearing and flipping. Experimental results indicate that YOLOv5 achieves a precision of 92.2%, recall of 89.2%, F1-score of 90.7%, and mAP@0.5 of 90.0%, while SSD achieves a precision of 88.5%, recall of 92.0%, F1-score of 90.2%, and mAP@0.5 of 91.4%. The comparative analysis demonstrates that both models are effective in detecting potholes across varied road textures and environmental conditions, with trade-offs between precision and recall. This study highlights the suitability of deep learning-based object detection models for automated road inspection, reducing human effort, enhancing maintenance efficiency, and improving road safety. The novelty lies in the systematic comparison of YOLOv5 and SSD under multi-weather conditions, providing practical guidance for intelligent transportation systems.