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IT Infrastructure Management and server downtime have been an area of exploration by researchers and industry experts, for over a decade. Despite the research on web server downtime, system failure and fault prediction, etc., there is a void in the field of IT Infrastructure Downtime Management. Downtime in an IT Infrastructure can cause enormous financial, reputational and relationship losses for customer and vendor. Our attempt is to address this gap by developing an innovative architecture which predicts IT Infrastructure failure. We have used a hybrid approach of human-machine interaction through Big Data, Machine Learning, NLP and IR. We sourced real-time machine, operating system, application logs and unstructured case notes into an algorithm for multi-dimensional symptoms mining, using iterative deepening depth-first search, traversal to create transactions for Sequential Pattern Mining of symptoms to events. It went through multiple statistical tests and review from technology experts, to create and update a dynamic Pattern Dictionary. This dictionary is used for training unsupervised and supervised classification models of machine learning, namely SVM and Random Forrest to score and predict new logs in a real time mode. The approach is also dynamic to use unsupervised clustering methods to give directions to the technicians on future or unknown pattern of errors or fault, to constantly update the Pattern Dictionary and improve classification for new IT products.