Abstract—Minimization of network downtime is the biggest challenge for service providers and one of its prime causes is equipment failure. On-time prediction and rectification of faults can reduce downtimes. Dynamic and very adaptive algorithms are required for processing huge torrents of data and the generation of predictions based on patterns and trends in the data obtained from trouble tickets and system logs. A novel strategy for fault detection based on the data accumulated has to be applied where the equipment behavior is monitored closely to prevent its failure and further prevent a network failure or downtime. Paper proposes Service Outage Prediction (SOP) that uses hidden Markov models (HMMs) which have a successful record in tasks related to pattern recognition and have been successfully used in the prediction of failures. The features of the aggregated fault data are subject to the supervised learning algorithm, in the initial phase of training. The samples are traced at different stages, and the failures are detected through high priority in tickets. Among the many solutions possible one of the best solutions being the approach of combining the Hidden Markov model and Bayesian Network. The results indicate the strengths of Hidden Markov Models as the probabilistic approach increases the accuracy of the prediction when compared to the other prediction algorithms. The likelihood of a customer raising a trouble ticket with high priority is predicted by the SOP model proposed.