The invention relates to the field of machine tool cutter remaining life prediction, and discloses a cutter residual life prediction method based on a machine learning regression algorithm. The cutterresidual life prediction method comprises two parts including model training and online life prediction. The model training comprises the steps of collecting original data of a complete life cycle and establishing a corresponding relation with the actual life of a cutter, preprocessing signals, extracting signal features to form feature vectors, performing cross validation to obtain an optimal cutter life model, and performing hyper-parameter adjustment and optimization. The online life prediction comprises real-time data acquisition, signal preprocessing, signal feature extraction to form afeature vector, input of THE optimal cutter life model based on optimal hyper-parameters and output of the residual life of the cutter. The number of eigenvalues extracted from each channel during model training is large, so that the training precision is high, the residual life of the cutter is accurately predicted, a residual life intelligent prediction model of the cutter is established, different regression models can be intelligently selected according to different working condition environments, and the model is good in generalization performance and high in portability.