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Water distribution systems continue to face persistent issues such as hidden leakages, corrosion, blockages, and structural cracks that disrupt supply and increase maintenance costs. These challenges often remain undetected due to the limitations of manual inspection, restricted accessibility, and the absence of continuous monitoring. The AI-assisted pipeline inspection robot is proposed as a compact, sensor-driven system that autonomously navigates inside pipelines to detect defects in real time through a structured, technology-driven approach. The robot integrates a Raspberry Pi camera, ultrasonic and temperature sensors, an IMU, GPS, and a sound-based vibration sensor to support live defect identification, distance measurement, environmental sensing, and positional tracking. Deep-learning-based YOLO detection enables the system to recognize corrosion, cracks, and blockages directly from the camera feed. A mobile application provides real-time video streaming, sensor visualization, Bluetooth- based robot control, and cloud-enabled data logging for analysis and maintenance planning. By enabling automated inspection, reducing manual intervention, and providing accurate defect detection, the AI-assisted robot improves the reliability of pipeline monitoring and supports proactive maintenance. Water authorities gain access to timely diagnostic data, enhanced operational transparency, and safer inspection practices, ultimately contributing to an efficient and resilient water distribution infrastructure