Orthodontics is undergoing a fundamental transition from a reactive, intuition-based specialty to a proactive, data-driven discipline. The integration of deep learning (DL), computer vision, large language models (LLMs), and physics-informed biomechanical simulations has catalyzed a new era of precision medicine. Between 2024 and 2026, the field has progressed from isolated diagnostic tools to multimodal, agentic AI ecosystems capable of orchestrating clinical workflows. Breakthroughs include automated three-dimensional (3D) cephalometric landmarking with sub–1.2 mm mean radial error, transformer-based multimodal extraction prediction models achieving AUCs above 0.94, physics-informed neural networks (PINNs) for aligner staging, and generative diffusion systems that dynamically simulate soft-tissue response. This review synthesizes recent advances in orthodontic AI, emphasizing the rise of multimodal foundation models integrating skeletal, dental, soft-tissue, and emerging genomic datasets. We further examine agentic AI for remote monitoring, federated learning frameworks for data sovereignty, explainable AI (XAI) to mitigate the “black box” dilemma, and the evolving regulatory landscape. Rather than replacing clinicians, AI augments decision-making capacity, positioning orthodontists as “pilot-in-command” of increasingly autonomous digital systems.