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Recent advances in artificial intelligence have led to the emergence of Physical AI, which interacts with the real world through robots and physical agents. However, conceptual definitions and system architectures for AI that perceive, interpret, and operate large-scale spatial environments—such as cities, national territories, and the Earth—have not yet been clearly established. This paper defines a new paradigm, Geo-Physical AI, which integrates digital twins and artificial intelligence to perceive, predict, and operate real-world spatial environments, and proposes a collaborative framework for its implementation.In the proposed Geo-Physical AI architecture, the digital twin layer replicates urban and national environments at high resolution and integrates terrain, infrastructure, transportation, environmental, and social data to support real-time visualization and scenario-based simulation. The artificial intelligence layer functions as a cognitive engine that learns from spatial data to recognize urban patterns, predict future risks, and derive optimal strategies across various domains, including traffic control, disaster response, and urban safety. Through the tight integration of these two technologies, the system continuously performs sensing, analysis, simulation, and execution in the real world.The framework consists of a three-layer collaborative structure: (1) a Digital Twin Layer responsible for spatial modeling and simulation, (2) an Artificial Intelligence Layer that performs pattern analysis, prediction, and decision optimization, and (3) an Execution Layer that connects analytical results to real-world services and policy implementation. Through application cases in transportation, disaster management, and urban safety, this study demonstrates that Geo-Physical AI enables a shift from reactive, post-event urban management to proactive, predictive, and preventive intelligent city operations.By conceptualizing and structuring Geo-Physical AI for the first time, this research provides the theoretical and technical foundation for realizing Cognitive Digital Twins that can autonomously perceive and respond to real-world condition