The current study optimizes the railcar diagnostic operations for minimum cancellations of maintenance and attainment of better reliability at a South African rail logistics company. The research shifts from time-based to condition-based and predictive maintenance approaches using machine learning techniques in combination with expert domain knowledge. The vibration spectrum analysis, for which abnormal amplitude was captured in the frequency domain, real-time monitoring of engine temperature and pressure represent the deterministic approaches. In that, deviations light up faulty conditions. Performance comparison of machine learning models shows that Random Forest, with 92% accuracy, outperforms XGBoost and SVM regarding the prediction of maintenance needs. These results propose the capabilities of predictive maintenance models in reducing life cycle costs by efficiently scheduling railcars, as well as raising the bar in terms of fleet reliability. The study also extends to explain how minor components may be used, like a winch of the brake system, in order to increase vehicle availability while reducing financial penalties related to vehicle downtimes. This proactive approach assures business delivery performance while enhancing operational efficiency in a critical rail logistics context.