While inherent system vulnerabilities cause pipeline failures, external factors exacerbate the issue . Climatic extremes and weather disasters, such as wildfires, become more frequent because of climate change and threaten drinking water infrastructures. In response, researchers leverage ML to better understand and predict the impact of these disasters on water pipes. Two ensemble ML models (RF and XGB) can predict the repercussions of calamities on water supply infrastructures (e.g., water pipelines) . These models incorporate 23 variables encompassing facility specifications and operational data from 419 water utilities in South Korea. The models project the total disaster index (TDI), a metric signifying the effects or damages wrought by three predominant disasters (typhoons, heavy rainfalls, and earthquakes) on water supply systems. While both RF and XGB have commendable predictive prowess concerning the TDI, XGB slightly outperforms in most scenarios. Another study developed four models, a linear regression-based repair rate (RR) method, LR, boosted regression trees (BRT), and RF, to predict pipeline damage during an earthquake . The models incorporate parameters such as ground shaking, permanent ground deformation, pipe material, pipe diameter, year of installation, and trench backfill type. Each model demonstrates unique strengths. The BRT model has the best overall predictive performance, while the LR model is instrumental in highlighting the influence of pipe materials and trench types on pipeline damage.