Journal of Engineering Research and Reports

Railway networks remain capital-intensive and safety-critical, yet maintenance still relies heavily on reactive and calendar-driven regimes that underexploit the condition data modern systems generate. Published machine-learning applications remain fragmented across asset classes and weakly connected to enterprise decision systems, leaving a gap this study addresses by developing an integrated framework uniting enterprise asset management, predictive analytics, and digital analytics for maintenance prioritisation and decision support. Adopting a quantitative design with a design-science orientation, the framework was implemented across stages spanning data preparation, feature engineering, modelling, evaluation, reliability translation, and decision integration. Two established condition sources supplied the binary classification and degradation estimation tasks. Random forest, extreme gradient boosting, support vector machines, and neural networks were trained with feature scaling, with synthetic minority oversampling applied within training folds for the classification task, then assessed through held-out testing and five-fold resampling, stratified for classification and partitioned at engine level for degradation. Gradient boosting achieved the strongest classification balance, recording an F1-score of 0.737 and a ROC-AUC of 0.981, while the neural network minimised degradation error at a grouped five-fold mean RMSE of 47.25 cycles. Resampling confirmed stability, attribution exposed speed, tool wear, and thermal differential as dominant drivers, and all ten highest-ranked observations were confirmed failures, a precision-at-10 of 1.00. Although benchmark analogues constrain generalisation, the contribution is advanced as a proposed architecture supported by a computational proof of concept, in which predictions are converted into auditable, risk-ranked maintenance priorities pending naturalistic validation on operational railway records.