Facial asymmetry poses a complex challenge in maxillofacial surgery, where aesthetics, function, and individual anatomy must be reconciled through precise planning and outcome evaluation. While most current methods for symmetry analysis have leveraged machine learning techniques, these approaches rely on large annotated datasets and often lack anatomical interpretability, making them unsuitable for patient-specific clinical use or in case of significant asymmetries. In this context, the presented work addresses this gap by introducing a computational mesh-based pipeline to construct a patient-specific symmetric facial mask, enabling both the prediction of symmetric outcomes and both the qualitative and quantitative assessment of surgical corrections, applicable even with limited data.