Resilience can be understood as the property of an object or system to recover from its state of disruption back to its complete functional stage as it was prior to the disruption event. There are different ways of measuring the resilience of a system and tracking the system performance is one of the methods. Thus, measuring the time taken by the system to recover to its original state is one of the parameters that can be considered. In this research work, we have focused on building a model(s) that predicts the time taken for an interdependent network to recover and function at 100%. In order to implement this idea, we present a case-study of the system of interdependent water, gas, and power utilities in Shelby County, TN. The model is trained using the train data set from the data set generated by running the optimization code multiple times and observing the time taken for the inter-dependent network to recover completely. The prediction of time to recover is made on the test data set using different models and the results are then compared.