This paper describes a predictive maintenance approach for monitoring the condition of Rexnord ZA-2115 double-row bearings utilizing vibration feature data. The first simulation tests demonstrated that the vibration variance is relatively stable, which means that the system’s functioning is within the normal. While the second and third simulation tests showed that the parameter dramatically increases, signaling degradation and failure, showing the importance of continuous monitoring and data-driven maintenance strategies to predict failures and minimize unexpected downtime. Determination of the Remaining Useful Life of the bearings provided a time to failure of 284.19 h with an accuracy of approximately 84.5% to the actual failure time using Python’s sci-kit-learn library and linear regression. The results presented in this paper underscore the importance of employing computerized maintenance planning methodologies to minimize unplanned outages and improve operational dependability.