Artificial intelligence (AI) is increasingly integrated into orthodontics, supporting tasks ranging from cephalometric landmark detection to treatment planning and remote monitoring. Evidence from recent literature shows consistent progress in diagnostic accuracy and workflow efficiency; however, its clinical impact remains dependent on validation, dataset quality, and regulatory clearance. Current applications demonstrate benefits in automated image analysis, multimodal data interpretation, and decision-support systems, yet real-world limitations persist. A major concern is restricted generalizability, as most models are trained on unicentric or homogeneous datasets, which raises risks of algorithmic bias and reduced reliability in complex or diverse clinical scenarios. Furthermore, ethical and legal factors, such as accountability, data privacy, and lack of standardized evaluation protocols, continue to limit large-scale adoption. Only a small proportion of AI tools have reached regulatory approval, underscoring the gap between experimental success and clinical maturity. This review synthesizes existing evidence, outlines key challenges, and highlights future directions, emphasizing the need for multicenter datasets, explainable AI systems, and structured regulatory frameworks to ensure safe and effective implementation in orthodontic practice.