Precision orthodontics, enhanced by artificial intelligence (AI) and machine learning (ML), is transforming how clinicians diagnose, plan, and execute orthodontic treatments. This article reviews emerging AI-driven technologies—including convolutional neural networks (CNNs), natural language processing (NLP), and reinforcement learning—and their integration into personalised orthodontic care. We present a unified Precision Orthodontic Framework, evaluate comparative AI performance metrics, and discuss clinical implementation barriers. Results demonstrate that AI systems achieve landmark identification accuracy exceeding 94%, reduce treatment planning time by up to 47%, and improve outcome prediction reliability by 31% compared with conventional approaches. Despite these gains, challenges such as data heterogeneity, regulatory compliance, and clinician adoption remain. A structured implementation roadmap is proposed to bridge the gap between laboratory innovation and routine clinical practice.