The overall performance metric of rotating machineries are governed by the reliability of bearings. Bearings are vital components for all moving parts. It has its presence in most of the equipments and machineries. Also, these bearings contribute to most of the failures or breakdowns in an industry. Failures can be reduced to a greater extent by selecting appropriate bearings that suit to the application. Nevertheless, after selection of right bearings, the failure in the bearings tops the list. It becomes complicated when we want to trace out the reasons for failures. Condition monitoring techniques are being deployed in order to increase the uptime of the machineries. Objectives: Strengthening the predictive maintenance by estimating the remaing useful life of bearings. Method: This paper proposes a predictive model to address the remaining life of the bearing that suits to a real time application. This method is validated on a laboratory experiment wherein the bearing is tested till it fails naturally at stated conditions. Findings: Thus obtained results show the model built using Support Vector Regression method proves to be effective in predicting the remaining life of the bearings. Applications/Improvements: The proposed predictive model is validated with the new set of data taken from experiments. This model can be deployed in critical real time applications where the bearing failure affects the performance of the machine. Addittionaly this model can be horizontally deployed for other critical components where continuous monitoring is essential.