This work aims to develop a fast and easy-to-use program for the prediction of brass instrument bifurcation diagrams, with minimal supervision from the user. Using numerical continuation, more than ten thousand bifurcation diagrams are generated to train a machine learning model with trumpet impedances as inputs, and descriptors associated with the bifurcation diagrams as outputs. Our approach is based on the definition of virtual players and virtual trumpets to generate the training data. Different regression models are then considered and their performance is compared. The model finally selected shows high speed and great accuracy in predicting the descriptors. Moreover, the regression approach includes regularization which promotes sparsity, hence improving the interpretability of the model. This program then constitutes a potential tool for music instrument designers to easily predict the dynamical behavior of numerical prototypes.