This study reviews radiographic data segmentation as a cornerstone of machine learning (ML) and deep learning (DL) in dentistry. After outlining artificial intelligence (AI), ML, and DL concepts, it highlights convolutional neural networks-driven tasks—classification, detection, and pixel/voxel segmentation—across panoramic, periapical, bitewing, and cone beam computed tomography imaging. Automated tooth numbering, restoration and implant labeling, caries delineation, endodontic morphology and fractures, periapical and periodontal lesions, and peri-implant bone loss show strong performance metrics, often matching or surpassing clinicians while markedly accelerating workflows. The study underscores AI’s potential to improve accuracy and efficiency while maintaining essential human oversight.