Polyvinyl chloride (PVC) pipes are used extensively in water infrastructure due to their lightweight, low cost, and ease of jointing. Failure occurrences in PVC pipes are attributed to poorly manufactured pipes, bad installation, excessive operating conditions, or third-party damage. Data-driven modeling techniques have been widely used in simulating and solving water infrastructure problems, specifically when the collected data are limited. The objective of this paper is to develop a data-driven modeling system that utilizes computational approaches and provides the analytical underpinnings to predict future PVC pipe failures. The system is based on data from El Pedregal City in Peru, simulation and regression algorithms, and supported by easy-to-perceive schematic representations. The hydraulic pressure and flow rate data are streamlined and fed to the regression machine. Subsequent to successive simulation iterations and various polynomial functions, the best fit model is selected. The efficacy of the model is investigated via different performance metrics, in tandem with a residual analysis scheme. The validation results revealed the robustness of the model with mean absolute error (MAE) of 0.35. The proposed model is a predictive tool that can be used by utility managers as a proactive measure against future pipeline bursts or leaks.