Real-time identification of road damage, particularly potholes, is essential for enhancing road safety and minimizing vehicle damage. Traditional road inspection methods are often labor-intensive and inefficient. To overcome these limitations, we propose an automated system that leverages deep learning to detect potholes in real-time from video input. This system can detect potholes under varying lighting conditions, specifically the YOLO (You Only Look Once) model, utilizing advanced computer vision techniques, including daylight, darkness, and vehicle headlights. The system aims to improve road monitoring by automatically identifying potholes and notifying authorities of their locations. By integrating geolocation services, the system pinpoints pothole locations using latitude and longitude coordinates and sends real-time alerts via a Telegram bot. Additionally, image enhancement techniques are employed to optimize detection performance in low-light conditions. The system has demonstrated high accuracy in detecting large potholes and identifying multiple potholes within a single frame. Through automated pothole detection and location sharing, this solution has the potential to significantly enhance road maintenance efficiency, thereby improving road safety and reducing accidents caused by road damage.