The field of water infrastructure monitoring and maintenance is witnessing significant developments, driven by the integration of physics-informed models, machine learning, and sensor technologies. Researchers are focusing on creating more efficient and scalable methods for fault detection, reliability analysis, and anomaly detection in water distribution networks and pipelines. The use of data-driven approaches, such as convolutional neural networks and one-class support vector machines, is becoming increasingly popular for leak detection and localization. Furthermore, the development of simulation software and AI systems, such as AquaSentinel, is enabling strategic sensor placement, real-time anomaly detection, and predictive maintenance. Noteworthy papers include: Modeling and Physics-Enhanced Fault Detection in Wastewater Pump Stations, which introduces a high-fidelity simulator for fault detection and isolation. Physics-Informed Neural Network-based Reliability Analysis of Buried Pipelines, which proposes an efficient method for reliability analysis using PINN-based surrogate models. AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture, which demonstrates 100% detection accuracy in experimental evaluation.