The underground aviation fuel hydrant systems have to tightly contain hydrocarbons, ensure that the fuel quality remains strict, and are difficult to reach because of the physical limitations involved. They are also influenced by the changes that take place when aircraft are refuelling. For these reasons, the method needed to monitor such networks is different from that applied to long-distance oil pipelines. This article looks at the research carried out between 2020 and 2025 on a number of topics including pipeline leak detection, structural health monitoring, fibre-optic sensing, acoustic emission, hydraulic analytics, sensor fusion, machine learning, and digital twins. On the basis of this research it proposes a framework tailored for airports in Saudi Arabia. The framework follows a step-by-step procedure: the pressure and flow data establish the hydraulic context, while acoustic, vibration, fibre-optic, hydrocarbon-sensitive and corrosion sensors each contribute separate evidence. Edge analytics are used to filter out operational noise, and then a fusion stage combines all the data to estimate the presence, location, severity and degree of degradation of any leak, as well as how confident the estimates are. A digital model links these results to the asset's history, inspection records, maintenance work and operational data. The framework places particular emphasis on the buried pipes underneath the aprons, the hydrant pits, the branch connections, the interfaces between the soil and concrete, access to the sensors, controlling false alarms, and distinguishing between the normal changes that occur during refuelling and abnormal leaks. The review also connects the framework to the EI 1560 best practices and to Saudi Vision 2030, an initiative which aims at achieving reliable, safe, environmentally responsible and digitally capable airports as air traffic increases. The key point is that Saudi airports should not depend on single-sensor leak detection; rather, a risk-based, multi-physics, data-fused approach is more reliable for providing early warnings, enabling condition-based maintenance, reducing product loss, and improving the predictive management of the hydrant network.