Abstract: A machine-learning-based prognostic and health management system comprises a machine sensor, an instruction receiver, a processor, and an annunciator. The machine sensor is configured to dynamically receive data of a machine under test associated with operations of the machine under test. The instruction receiver is configured to dynamically receive a model-assigning command. The processor is configured to dynamically apply a damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict an anomaly probability of an anomaly occurrence of the machine under test. The processor also dynamically generates, according to the anomaly probability, a damage possibility warning on the machine under test, and determine whether to keep the machine under test running or not.