Bearing faults are one of the most vital parts of rotating machinery. These faults cause operational inefficiencies, unplanned downtime, and huge maintenance costs. A bearing fault is when the bearing detects, degrades, or malfunctions in its operation, which impairs normal functioning. Diagnosis of the faults is necessary to ensure safety and normal operations. Detection and identification of faults, at an early stage is considered essential for ensuring reliability and avoidance of machine breakdown and failures. The study discussed below illustrates the performance of predictive models for bearing faults by implementing more advanced algorithms to introduce real-time detection of faults and consequent predictive maintenance using various machine learning methodologies, such as SVMs, LSTMs, and neural ODEs. This paper proposes an intelligent, automated model for predicting the failure of bearings in such a way that can predict the failure in bearings before the actual failure happens. The standard metrics used will be accuracy, precision, and recall in measuring the performance of the proposed solution.