Predicting the health of digital infrastructure is a vital issue to minimize downtime to maintain a high service level. This research work has applied predictive analytics to forecast future health of complex-IT (Information Technology) based infrastructure. Every subset of a complex IT infrastructure is at the level of a single machine, that tracks the run-time status of the system and generates electronic messages, error events, and further manual massages as ticket logs. This research has suggested a 3-step method to build a novel predictive analytics model using text mining algorithm for extracting features from the log data. Then, it provides a model for selecting the critical devices in the system and predicting their failure. The final step suggests a forecasting model to predict the health of infrastructure for a given timestamp. The models in the second step of this integrated approach are built using algorithms such as association rules and rank based algorithm. The time-series model is built using a machine learning methods (ANN and SVR). This approach can be readily applied to many other types of information technology-based medical, banking, energy infrastructure, and other applications.