BACKGROUND: Segmentation of airways and soft tissues on panoramic radiographs is a challenging yet crucial task in dental diagnostics, as these regions can often be confused with fractures or other lesions due to superimposition. This study aimed to perform segmentation of both airways and soft tissues on panoramic radiographs simultaneously using an artificial intelligence (AI)-based model. METHODS: Segmentation masks were created by annotating the nasal, oral, and oropharyngeal airways, along with the tongue, soft palate, and uvula, on 1,004 panoramic radiographs. Data augmentation and image processing techniques were applied to enhance dataset diversity. Of the radiographs, 72% were allocated for training, 18% for validation, and 10% for testing. A custom AI model based on the ResUNet architecture, comprising 74 layers and 24.3 million parameters, was developed utilizing the TensorFlow library. Performance metrics, including accuracy, precision, sensitivity, specificity, F1 score, intersection over union (IoU), and mean average precision (mAP) were evaluated. RESULTS: The areas AI model achieved an accuracy of 0.979, precision of 0.869, sensitivity of 0.870, specificity of 0.925, F1 score of 0.870, IoU of 0.777, and mAP of 0.500. Intra-observer agreement values ranged from 0.762 to 0.958. CONCLUSIONS: To our knowledge, this is the first study to develop an AI -based model for segmentation of airways and soft tissues on panoramic radiographs. The proposed algorithm demonstrated high accuracy in identifying the regions of interest, enabling rapid and efficient radiographic analysis. This model has the potential to enhance decision support systems and reduce the risk of misdiagnosis. CLINICAL TRIAL NUMBER: Not applicable.