Orthognathic surgery is often required to address moderate to severe skeletal class II malocclusion, a condition that affects both facial aesthetics and function. Traditional diagnosis relies on cephalometric radiographs and expert evaluation, which can be time-consuming and subject to inter-observer variability. The need for AI-assisted initial screening based exclusively on lateral soft tissue profiles is increasingly recognized, particularly for identifying mandibular retrusion relative to the maxilla. This study aimed to develop and evaluate a deep learning-based model for classifying lateral facial profile photographs into surgical (S) and non-surgical (NS) categories. A dataset of 524 standardized profile images from skeletal class II patients was collected from three dental centers. Each image was independently reviewed by a panel of five experts (three orthodontists and two maxillofacial surgeons), with labels assigned based on majority consensus. To minimize visual bias and enhance model performance, images underwent preprocessing involving facial cropping, background removal via U-Net segmentation, silhouette contour extraction, and resizing to 128 × 128 pixels. A custom convolutional neural network (ContourNet) was developed and optimized using fivefold cross-validation. The model achieved an overall accuracy of 90%, with a precision of 92% for NS and 88% for S, and a recall of 88% for NS and 92% for S. Saliency maps revealed that the model focused on clinically relevant regions, particularly the lower jaw and chin in surgical cases. This deep learning framework demonstrates strong potential as a non-invasive, efficient tool for preliminary screening in orthognathic surgery decision-making. Further external validation on larger and more diverse populations is recommended prior to clinical implementation.