Surface-enhanced Raman spectroscopy (SERS) is a powerful technique that can capture the electronic-vibrational "fingerprint" of molecules on surfaces. Ab initio prediction of Raman response is a long-standing challenge because of the diversified interfacial structures. Here we show that a cost-effective machine learning (ML) random forest method can predict SERS signals of a trans-1,2-bis (4-pyridyl) ethylene (BPE) molecule adsorbed on a gold substrate. Using geometric descriptors extracted from quantum chemistry simulations of thousands of ab initio molecular dynamics conformations, the ML protocol predicts vibrational frequencies and Raman intensities. The resulting spectra agree with density functional theory calculations and experiment. Predicted SERS responses of the molecule on different surfaces, or under external fields of electric fields and solvent environment, demonstrate the good transferability of the protocol.