In this context, the combination of ML schemes with classical force fields turns out to be an appealing idea. To achieve the best possible balance between the computational cost and the required physical accuracy, hybrid schemes that combine QM with MM forcefields have emerged in the last decades (multiscale QM/MM methods). Such QM/MM schemes have contributed to different research fields such as the study of reactive processes in aqueous solution, enzymatic catalysis, photo-chemical processes in complex environments, drug discovery and others. The bottleneck in QM/MM simulations is determined by the computational cost of the electronic structure method used to described the so called QM region, i.e., the portion of the system which is treated with QM. While an increasing spectrum of programs that exploit the high computing power of graphical process units (GPUs) have reduced the gap in the computational cost between the QM and the MM region, the former one remains undoubtedly as the most decisive factor. In this context, ML methods provide an exciting next step in speeding up the simulations of molecular systems in complex environments, where QM/MM approaches constitute the state of the art in terms of methodology.