Engineers rely on accurate knowledge of road conditions to ensure smooth traffic flow. Civil engineers specifically invest significant effort in identifying road cracks, including analyzing their size, shape, and location coordinates. With the proliferation of digital cameras, GPS tracking systems, and advancements in image processing, the challenge of road crack detection can be addressed through Artificial Intelligence (AI) and Image Processing techniques. This research introduces a Convolutional Neural Network (CNN) designed to identify road cracks in images captured by vehicle-mounted cameras. The CNN module, deployed as a component of the system, functions alongside a client-side module affixed to the moving vehicle. The client-side module captures images and sends them, along with location coordinates, to the server for analysis. A client–server-based model has been developed in this study, capable of tracking road conditions in real-time. The system employs AI-based image processing to detect road cracks, facilitating timely interventions for maintenance and repair. Moreover, the server-side component is equipped to generate and transmit SMS notifications to relevant authorities, alerting them to the identified road issues. The integration of AI, image processing, and a client–server architecture presents a promising approach to enhance road infrastructure management. By enabling real-time monitoring and proactive maintenance, the developed model offers potential benefits for traffic management, safety, and infrastructure sustainability.