A method for prognostic maintenance in semiconductor manufacturing equipments is disclosed. The said method comprising: collecting a plurality of raw data from the default detection and classification system for equipments, preprocessing the raw data, using the neural network model (NN model) to find a plurality of health indices, generating health information by using the principal component analysis (PCA) to identify the health indices, and using the partial least square discriminated analysis (PLS-DA) to find a health report. The health report provides the engineers with current risk levels of equipments. By the health report, the engineers can initiate prognostic maintenance and repair the equipments early.