The diagnosis of jaw lesions poses a significant challenge in oral and maxillofacial radiology due to overlapping imaging features, interobserver variability, and the heterogeneous biological behavior of these pathologies. Machine learning (ML), a subset of artificial intelligence, provides computational methods that learn from imaging data to perform classification, detection, segmentation, and prognostic prediction with reduced subjectivity. This chapter explores the principles, workflows, and clinical applications of ML in detecting and classifying jaw lesions using radiographic modalities such as panoramic radiographs and cone-beam computed tomography (CBCT). Key ML algorithms, including Logistic Regression, Support Vector Machines, Random Forests, k-Nearest Neighbors, and Naïve Bayes, are discussed with respect to their methodological strengths and limitations. This chapter also examines preprocessing techniques, feature engineering, model training and validation, and performance evaluation strategies critical to ensuring clinical reliability. Emerging trends such as transfer learning, federated learning, explainable AI, and volumetric learning are presented to illustrate future directions in this evolving field. By bridging computational methods with clinical needs, ML holds the potential to support earlier, more accurate, and reproducible diagnosis of jaw lesions, thereby enhancing patient care and guiding precision treatment planning.