Water quality monitoring and sewage conservation are vital in addressing pollution and urbanization challenges. Traditional methods of sewer maintenance are often inefficient, prompting the need for advanced automated solutions. This paper examines the application of Artificial Intelligence (AI) and Mobile Robotics in sewage conservation, with a focus on the YOLO (You Only Look Once) object detection models. YOLO’s real-time detection capabilities provide significant advantages in identifying sewer blockages such as grease, plastics, and tree roots. We explore the evolution of YOLO models, from YOLOv3’s Darknet-53 architecture and feature pyramid networks to YOLOv4’s CSPDarknet53 and PANet, which enhance detection accuracy and efficiency. These models optimize object detection at different scales, offering improved performance for real-world sewage monitoring. The integration of YOLO-based robotic systems into urban infrastructure reduces the need for manual intervention, improves operational safety, and contributes to sustainable urban development. These AI-driven systems represent a significant leap forward in automating sewage management and ensuring effective water quality monitoring.