In traditional overhead transmission line fault monitoring, traveling wave signals are not captured in time and the processing accuracy is insufficient, which seriously restricts the accuracy of fault location and the efficiency of power grid operation and maintenance. To address this problem, this paper constructs a real-time traveling wave monitoring system based on the Internet of Things (IoT) and cloud computing architecture and builds an efficient data processing link through the end-edgecloud collaborative mechanism. The perception layer uses low-power IoT terminals to collect high-frequency electrical disturbance data. The edge layer deploys lightweight AI (Artificial Intelligence) models to extract signal features and trigger anomalies, significantly reducing the cloud load through localized preprocessing. The cloud relies on a distributed timing analysis engine to achieve deep fusion and precise positioning of multi-source data. Experimental results show that the accuracy of the designed system in 5G environment is 98.6% in traveling wave recognition; the fault location error is 110 m; the real-time response delay is only 28 milliseconds. It meets the monitoring requirements of high-precision and high realtime performance. The end-edge-cloud collaborative mechanism proposed in this paper provides a stable and efficient technical path for transmission line status perception and intelligent fault handling.