Traditional orthognathic surgery planning has long faced challenges in accurately predicting post-surgical facial changes and aesthetic outcomes. Conventional methods rely heavily on two-dimensional imaging, manual measurements, and the clinician’s subjective judgment, which often lead to discrepancies between predicted and actual results. Such limitations can affect both functional and cosmetic outcomes, reducing patient satisfaction and treatment predictability. With the advancement of artificial intelligence (AI) and deep learning technologies, new possibilities have emerged to overcome these challenges. Convolutional Neural Networks (CNNs), a subset of deep learning algorithms, have shown exceptional capability in analyzing complex facial structures and predicting surgical outcomes with high precision. By processing large datasets of pre- and post-operative images, CNNs can identify subtle patterns and correlations that are difficult for humans to detect. This allows for more accurate simulation of facial morphology and bone movement during surgery planning.