Although ML methodologies have not been visited as a solution for real data, not only are they capable of performing the prediction in a meaningful time, but also they need less expertise for configuration. Besides the mentioned advantages, the ability of discovering the hidden correlation between different features distinguish ML from the other possible methodologies to solve this problem. In , Bessa et al. were able to use regression models to predict the deformation parameters of some of the physical equations proposed by Chen et al.  and model the breast shape according to the degree of deformation defined by the user. Despite the promising results, it was limited by the usage of a completely synthetic dataset and by the dependence on previous knowledge of the physical equations of deformations. Here, the aim is to expand the described work by using real data, avoiding the usage of specific physical equations to model the breast deformations. Besides, to accomplish the requirement of providing a dataset with the demanded properties, an in-house dataset is generated using data from Magnetic Resonance Imaging (MRI) of real patients and an available breast cancer surgery simulator .