The invention discloses a machine fault prediction method based on MFCC feature extraction, and belongs to machine fault prediction methods. The machine fault prediction method comprises the steps that the feature of a current acoustical signal of running of a machine is obtained through an acoustical sensor installed on the machine, the acoustical signal is preprocessed, and then Mel conversion is carried out on the preprocessed acoustical signal to obtain an MFCC feature vector of the acoustical signal; according to the obtained MFCC feature vector, prediction is carried out on the health condition of the machine, the specific clustering process is that a SVM conducts clustering on the MFCC feature extracted when the machine runs and stored sample data obtained when the machine runs normally, the clustering result is analyzed through a vote method, and then the machine fault is predicted. The machine fault prediction method based on the MFCC feature extraction has the advantages that the acoustical feature of the machine is extracted and converted into the Mel domain, then clustering analysis is carried out on the feature vector through the SVM, the health condition of the machine can be rapidly, accurately and easily predicted, operation is easy, prediction precision is high, the prediction speed is high, the anti-noise performance is good, and nonlinear, random and time-varying signals can be accurately predicted.