<p>The issue of potholes and bad road quality is faced by nearly every developing country. These conditions often lead to accidents and traffic and cause general public inconvenience. Significant research has been performed in the field of effective pothole and road quality detection, but owing to several underlying issues such as relying on only computer vision or inertial sensor-based modeling, or requiring specialized equipment such as LiDAR (Light Detection and Ranging), implementing these solutions in the real world has never been feasible, especially in developing countries where a reliable system is required that works under any condition and, more importantly, can be implemented with minimal resources and changes to the existing infrastructure. The proposed system is an end-to-end robust architecture that uses mobile devices mounted on government vehicles for data collection. It features an innovative ensemble of two object detection models, YOLOS (You Only Look at One Sequence) and YOLOv8 (You Only Look Once), resulting in a 97.34% mAP@0.50 (mean Average Precision) score for pothole detection (a nearly 6% improvement over the best state-of-the-art model) in conjunction with state-of-the-art inertial sensor-based pothole detection and road quality detection models, delivering impressive accuracies of 98.5% and 96.3%, respectively. This two-forked approach to pothole and road quality detection combined with innovative connected applications like an analytics dashboard for the government and a navigation application for the consumer make this entire system highly relevant while being scalable, reliable, and easily deployable using the existing infrastructure in developing countries.</p>