Detecting leaks in buried pipe network systems poses a challenging task due to their concealed nature. Trained experts traditionally employ hearing rods to pinpoint leak sources, a scarce skill set. Escalating water supply aging and diffusion rates exacerbate the growing number of leaks, particularly concerning in developing countries where expert labour is costly. Global daily water leakage, estimated at 220 million tons, could meet the drinking water needs of 880 million people.  To revolutionize leak detection, artificial intelligence (AI) taps into a wealth of expert-validated leak sounds, discerning patterns in noise data emitted from buried pipes. Our AI-driven leak detection system capitalizes on this concept. WI.Plat studied leak and ambient sounds from 137,424 sites in 20 Korean small cities. By fusing Internet of Things (IoT) and cloud technologies, the company captured on-site leak sounds in real-time. These were relayed to the cloud for AI analysis, unveiling distinct leak signatures. Results were then channelled through the cloud to a mobile app used by field workers who collected the initial sound data. This pioneering setup enables swift validation by non-experts, transforming leak detection efficacy.