Artificial intelligence (AI) is rapidly invading healthcare sectors. Advances in technologies offered by AI and their integration into various routine tasks in the health field are constantly evolving. This study aims to systematically synthesize current research and the influence of different artificial intelligence algorithms on the exploration of the mandibular condyle, or on the study of the different pathologies that can affect this part of the TMJ (displacements, fractures, temporomandibular joint osteoarthritis), by using medical imaging (MRI, CBCT, OPG) and/or other types of datasets (Clinical, Biological, Radiomics…). Several databases (PubMed, Web of Science, Scopus, Science Direct, Springer, Research Gates and Google Scholar) were consulted for articles on the subject of « Mandibular Condyle and Artificial Intelligence », from 1991 to 2024. Four hundred and seven (407) studies were identified, of which (33) thirty-three were included, totaling more than 12,610 patients between controls and with TMD groups and more than 46,241 images (about 10,083 MRI, 23413 CBCT, and 12,745 OPG), without forgetting clinical, biological radiomics features, and craniomaxillary variables. To achieve this goal, various artificial intelligence models were used, Deep Learning (ANN, Inception V3, U-Net, Seg-Net, ResNet 101, XCeption, VGG16, YOLOv7…), Machine Learning (Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes, Light GBM, XG Boost.