The production systems are affected by degradations and failures caused by operational and environmental conditions. Indeed, unplanned, unscheduled maintenance (UUM) practices lead to lower productivity, lost production, expensive labour (call-outs, overtime) and damaged equipments. However, predictive maintenance (PdM) is a condition-based maintenance strategy (CBM) that has become popular among practitioners in recent years that carries out maintenance action when needed, avoiding unnecessary preventive measures or failures. Machine learning (ML) has become an advanced diagnostic approach in maintenance strategy to mitigate those challenges. Despite that, the ML-based PdM strategies are infancy stage due to the immaturity of the Industrial Internet of things (IIoT) which is responsible for data collection and execution. Implementing ML-based PdM is a difficult and expensive process, especially for those companies which often lack the necessary skills and financial and labour resources. Thus, a cost-oriented analysis is required to define when ML-based PdM. The proposed study develops the ML algorithm and provides an intelligent PdM model with a framework for practitioners and researchers considering aircraft industries. The contribution of the paper has twofold. The first is to developing an ML-based predictive maintenance model. The analysis shows that  random forest outperformed other models scored 28.63 cycles to predicts Time to failure (TTF) within the average error range of ±28 cycles. While, the research finding helps to monitor that how to execute data wrangling and prepare pandas data frames to define TTF of the airlines engines.