Urban roads, especially in populated cities like Mumbai experience daily wear and tear and get damaged quickly, leading to the formation of potholes. Conventional methods like manual inspection and laser-based systems for the detection of potholes are labor intensive, time consuming and costly, making deep learning models an efficient anfad cost-effective solution for automating pothole detection and road maintenance. This research presents a pothole detection system using YOLOv8 (You Only Look Once Version 8), a deep learning model that performs well in real time object detection by balancing the speed and accuracy of detection. It improves upon its previous model versions such as YOLOv2, YOLOv3, YOLOv4, YOLOv5 and YOLOv7 where the earlier versions achieved a Mean Average Precision (mAP) of 85–90%, while later iterations such as YOLOv5 and YOLOv7 improved the mAP to 94%. In the pothole detection system, we have also integrated a notification system that sends an SMS (Short Message Service) alert with the Global Positioning System (GPS) Coordinates i.e. longitude and latitude of the pothole once detected; indicating its potential to contribute to road safety improvements and more stream-lined infrastructure maintenance processes. While the current system sends notifications to a personal contact number for demonstration purposes, the system is designed to create real-world impact by potentially sending the notification alert to a designated government portal contact number, enabling faster intervention and more efficient road maintenance. As a whole, the pothole detection system achieves a precision of 92.7% and a recall of 87.5%, ensuring that potholes are detected with minimal false positives.