The pipeline plays an important role in transporting liquid metals over long distances. However, due to the harsh conditions on the construction site, the pipelines are always exposed to structural damage due to corrosion, damage, etc. These pipelines are often vulnerable to natural and third-party events such as explosions, earthquakes, explosions, drilling and vehicle traffic. Methods for monitoring liquid metal pipes include the use of continuous pipe assessments and pipe integration management systems. One of the biggest challenges faced by liquid metal pipeline system in the past has been the real-time monitoring of seamless pipes at certain locations. Previous studies of liquid metal pipeline monitoring have rarely focused on real-time wireless data transmission and data monitoring in the Internet of Things (IoT) operating system. In this paper, we propose an optimal liquid metal pipeline damage detection using IoT sensor platform and hybrid soft computing techniques. The proposed work consists of two fold systems such as data collection and data processing unit. In data collection unit, we utilize IoT sensors deployment with the multimedia sensors to gather liquid metal pipeline images which improve the detection accuracy. In data processing unit, we introduce a hybrid cat hunting based neural network (hybrid CHNN) to detect and localize the pipeline damages/cracks to avoid unwanted leakage and accidents. Finally, we evaluate the performance of our proposed hybrid CHNN detector with the different test samples and the simulation results are compared with the existing stateof- art pipeline damage detectors in terms of accuracy, precision, Recall and F-measure.