This article introduces the core concepts of machine learning, deep learning (DL), and active learning (AL) and their impact on modern dentistry. It explains how these artificial intelligence technologies enable automated analysis of complex dental data, including the detection and segmentation of periapical lesions from cone-beam computed tomography scans. Emphasis is placed on DL models such as convolutional neural networks and transformers, the role of AL in reducing annotation burden, and knowledge-informed strategies that incorporate anatomic rules. Together, these methods are shaping the future of dental diagnostics, treatment planning, and clinical decision support.