The integration of artificial intelligence (AI) with Internet of Medical Things (IoMT) devices in dental implantology has created unprecedented opportunities for real time monitoring, predictive analytics, and personalized treatment. However, this convergence also expands the attack surface across device communication, clinical infrastructure, and decision support pipelines. Traditional perimeter based security models are inadequate for the distributed, dynamic, and latency sensitive nature of modern dental implant networks. This paper presents a zero trust security framework specifically designed for AI enabled IoMT networks in dental implantology. The framework integrates three core innovations: (1) a hierarchical zero trust architecture with self sovereign identity based device admission and continuous verification, (2) a federated learning based intrusion detection system that preserves patient privacy while enabling collaborative threat intelligence across institutions, and (3) a publicly auditable evidence chain using post quantum cryptography and blockchain anchoring for tamper evident accountability. We validate the framework using a simulated dental implant monitoring network with 50 edge nodes, 200 IoMT devices, and three threat scenarios. Results demonstrate intrusion detection F1 score of 0.978, zero trust access control with sub 100ms latency, and complete audit trail verifiability. This work establishes that zero trust principles can be effectively applied to secure AI driven dental implant networks while maintaining clinical performance requirements.