Looking back to the objective of predicting breast shape after BCS, the expected prediction should be performed considering the points of both pre- and post-surgery models, together with clinical features. Therefore, the possible learning approach to be proposed must be able to deal with large number of inputs (points), and correlate them with the features (clinical features). In ensemble learning the key idea is that different algorithms explore different search spaces and hypotheses, so composite systems could outperform single ones . The strategy of exploiting ensemble learning methodology assures to gain improvement of not only the robustness, but also the performance of the learners via combining the votes of stronger single regressors combined to build the prediction model. As an ensemble learning approach, tree-based learners provide an appropriate framework (both in time and performance evaluations) to take part in training with large number of inputs. Therefore, within this research, tree-based learning methodologies are taken into consideration to perform the required regression in finding the predicted coordinate of breast shape.