Artificial intelligence (AI) is transforming orthodontics by enhancing diagnostic accuracy, treatment planning, and patient monitoring. This review explores AI applications in orthodontics, including dental diagnostics, cephalometric analysis, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision-making, and telemonitoring. AI tools, such as Diagnocat and CephX, demonstrate high accuracy in identifying dental pathologies, cephalometric landmarks, and skeletal age. Deep learning algorithms, particularly convolutional neural networks (CNNs), show promise in medical imaging, including orthodontic diagnosis and treatment planning. AI also aids in TMJ evaluation, extraction decision-making, and orthognathic surgery planning, with studies showing high diagnostic accuracy. However, AI's performance varies, and manual supervision is necessary to ensure reliability. Challenges include data heterogeneity, the need for continuous learning, and ethical concerns. While AI offers significant benefits, its integration into clinical practice requires careful implementation, governance, and validation to ensure patient safety and equitable access. The review highlights the potential of AI to improve orthodontic care but emphasizes the need for further research and cautious application.Artificial intelligence (AI) is transforming orthodontics by enhancing diagnostic accuracy, treatment planning, and patient monitoring. This review explores AI applications in orthodontics, including dental diagnostics, cephalometric analysis, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision-making, and telemonitoring. AI tools, such as Diagnocat and CephX, demonstrate high accuracy in identifying dental pathologies, cephalometric landmarks, and skeletal age. Deep learning algorithms, particularly convolutional neural networks (CNNs), show promise in medical imaging, including orthodontic diagnosis and treatment planning. AI also aids in TMJ evaluation, extraction decision-making, and orthognathic surgery planning, with studies showing high diagnostic accuracy. However, AI's performance varies, and manual supervision is necessary to ensure reliability. Challenges include data heterogeneity, the need for continuous learning, and ethical concerns. While AI offers significant benefits, its integration into clinical practice requires careful implementation, governance, and validation to ensure patient safety and equitable access. The review highlights the potential of AI to improve orthodontic care but emphasizes the need for further research and cautious application.