The prediction of the post-surgery shape of the breast after surgical intervention is a complex task that requires modelling the influence of several factors on the aesthetic outcome of surgery. Approaches to model these deformations are typically based on biomechanical models. However, FEM takes longer than expectations, from hours on high-end machines, up to some days on normal computers used in clinics. In this work, an alternative strategy-based on machine learning techniques is proposed which overcomes the timing demands of biomechanical simulation, keeping most of the properties and characteristics of the breast.