Data centers (DCs) are critical for artificial intelligence (AI) and the digital economy, with AIDCs introducing new operational challenges. This research proposes a novel physical AI (PhyAI) framework to advance DC operations and management. The system features three core modules: an industry-grade in-house DC simulation engine for high-fidelity AIDC modeling, an AI engine built on NVIDIA PhysicsNeMo for training and evaluating physics-informed machine learning models, and a digital twin platform based on NVIDIA Omniverse. This framework enables the creation of real-time digital twins to digitalize, optimize, and automate future DC operations. A case study demonstrated its effectiveness in predicting thermal and airflow profiles for a large-scale DC in real-time, achieving a median absolute temperature prediction error of 0.18 °C, outperforming traditional computational fluid dynamics and heat transfer (CFD/HT) simulations. This emerging approach would open doors to several potential research directions for advancing PhyAI in future DC operations.