With the rapid development of computer science, the application of artificial intelligence (AI) in the field of medical imaging has become increasingly extensive. The temporomandibular joint (TMJ) is structurally complex, with a high incidence of related disorders and diverse clinical manifestations. This review analyzes the current state of research on AI in TMJ imaging diagnosis. Deep learning models based on U-Net and its derivatives have demonstrated outstanding performance in segmentation of condyle and articular disc. Various object detection and feature extraction algorithms have shown excellent diagnostic efficacy for common conditions, such as osteoarthrosis and disc displacement, with some models even achieving expert-level performance on test datasets. Meanwhile, explainable AI provides intuitive justification for model decisions through techniques such as heatmap visualization. Notably, current studies still face critical challenges, including coverage of disease spectra, integration of multimodal data, and model generalizability. Future studies should focus on developing integrated systems that combine diagnosis, segmentation, generation, and interpretation functions. Through multicenter data validation and algorithmic optimization, these efforts will enhance the clinical applicability and decision transparency of models, ultimately laying the foundation for precise imaging diagnosis and intelligent management of TMJ disorders.