The accurate diagnosis of jaw lesions continues to pose significant challenges in oral and maxillofacial radiology due to overlapping radiographic features, variability in interpretation, and the diverse biological behavior of cysts, tumors, and fibro-osseous conditions. Deep learning, an advanced subset of machine learning, has transformed medical image analysis by allowing neural networks to automatically extract hierarchical features directly from imaging data. Unlike classical machine learning models that rely on handcrafted features, deep learning offers end-to-end solutions for classification, detection, segmentation, and prognostic prediction. This chapter explores the principles, workflows, and clinical applications of deep learning in maxillofacial radiology, with a focus on modalities such as panoramic radiographs, cone-beam computed tomography (CBCT), computed tomography (CT), and magnetic resonance imaging (MRI). Core architectures including convolutional neural networks, three-dimensional CNNs, autoencoders, vision transformers, and generative adversarial networks are discussed with reference to lesion detection, anatomical segmentation, temporomandibular joint evaluation, and quantitative MRI analysis. This chapter also examines preprocessing strategies, model training and validation methods, hybrid radiomics–deep learning approaches, federated learning, and explainable AI frameworks aimed at improving transparency and clinical acceptance. By integrating computational intelligence with clinical needs, deep learning holds the potential to enhance diagnostic precision, support personalized treatment planning, and advance the role of artificial intelligence in maxillofacial imaging practice.