An improved solution accurately predicts of an underground pipe's likelihood of leaking. A data-driven approach uses a combination of information acquisition, classification, regression and/or machine learning. The replacement of underground pipes can be prioritized. Pipe data is inputted and processed. Potential features within the cleaned data is used in pipe life of failure prediction models. The importance of the potential features is ranked. The most important features are extracted and applied to a likelihood of failure model that is created based on historical data and machine learning. Future likelihood of failure for each pipe in the network of pipes can be predicted using the model.