Road damages including cracks and potholes pose significant challenges to road safety. Automated systems are being developed to address these issues. We use deep learning algorithms to analyze images of road surfaces to identify cracks and classify pothole roads. The model’s capacity to adjust to various road conditions gives its capability for real-time use in the identification of road damage. This achievement creates opportunities for additional research into deep learning models, to improve their capacity to handle more significant issues with infrastructure upkeep and safety. Deep learning models can be very helpful in detecting these potholes mainly on busy roads with hectic traffic or even small roads. Combining the advanced model, VGG16 improves the effectiveness of road defect-detecting systems. This would be very helpful for the reconstruction of roads. With all these the effectiveness of applying deep learning methods—more especially VGG16—for detecting road conditions. The groundwork establishes future developments with deep learning to road infrastructure monitoring and maintenance while also adding to the research in intelligent transportation systems.