A Review of IoT-Based Machine Health Monitoring and Predictive Maintenance Using Machine Learning: Architectures, Algorithms, and Open Challenges

Unplanned equipment failure remains one of the most expensive problems in modern manufacturing, and the maintenance strategy adopted by a plant has a direct bearing on its productivity, safety, and operating cost. Traditional reactive and time-based preventive approaches either allow machines to run to failure or replace healthy components too early, and neither uses the rich stream of data that industrial assets now generate. The convergence of the Industrial Internet of Things (IIoT) and machine learning has made a third option practical: predictive maintenance, in which the condition of a machine is monitored continuously and its future health is estimated from sensor data. This paper reviews the state of the art in IoT-based machine health monitoring and machine-learning-driven predictive maintenance. We examine the layered sensing-to-cloud architectures that underpin such systems, survey the feature-extraction and learning methods that have been applied to fault classification and remaining-useful-life estimation, and compare representative studies spanning support vector machines, ensemble methods, deep networks, image-based diagnosis, and survival analysis. Reported accuracies range from roughly 88% for a single-classifier failure detector to 96% for a vision-based gearbox diagnostic, while survival models achieve concordance indices above 0.97 on real industrial data. Drawing these threads together, we identify a set of recurring gaps: an over-reliance on single algorithms and balanced benchmark datasets, the fragmented treatment of fault classification and life estimation, the limited adoption of survival analysis in an Industry 4.0 setting, and unresolved questions around edge deployment, security, and interpretability. We close by outlining an integrated research direction that unifies real-time IoT sensing with a combined classification-and-prognostics pipeline.