The learning process was divided into two main tasks: feature inspection and model selection. In the first task, the complex interplay of clinical features for conditioning the breast shape after surgery healing was untangled by comparing the influence of different sets of features in the deformed breast after resulted from BCS. Here, it was concluded that breast density had the highest impact on the breast displacements while the quadrant where comprises the tumor was determining to predict the directions of shape adjustments. Further analyses of the importance of features resulted from learning process reinforced the experimental findings. In the model selection task, several regression methodologies, including Random Forest, Gradient Boosting Regression, and Multi-Output Regression were studied. Beside the learning model, a heuristic model was also proposed to validate the veracity of learning methodologies and understand the magnitude of the distances. Additional investigations were conducted with respect to sample PCL data, as well as a complementary study to assign different weights to the training instances, which resulted in predictions with slightly better evaluations. The numerical evaluations with the pair-wise and global distances indicated that the RF regressor constructed with the adaptive weighted training set outperformed MOR and GBR in both lines of average and Hausdorff OFs.