In orthognathic surgery, post-surgical facial appearance plays a critical role in both patient quality of life and mandibular function. However, existing methods typically rely on labor-intensive skeletal surgical plans to predict post-surgical facial shapes, rather than guiding the planning process using appearance-based objectives. To address this limitation, we propose the Face Model Assisted Regression Network (FMR-Net), which leverages the FLAME parametric face model to generate anatomically coherent facial representations. The FLAME model provides a topologically consistent mesh structure, enabling the network to learn structured facial information across subjects through effective loss computation. Furthermore, we introduce a region-weighted surgical loss that incorporates clinical prior knowledge from surgeons, and a Laplacian smooth loss to ensure surface smoothness. Experiments on skeletal Class II orthognathic surgery data demonstrate that our method outperforms state-of-the-art approaches in both quantitative accuracy and qualitative results for facial shape prediction.