This paper introduces a purely theoretical framework that fuses quantum information theory, general relativity and machine learning to construct a high-energy intelligence model with direct application to large-scale social systems. By leveraging entangled quantum networks in curved spacetime and high-energy field dynamics, the proposed model formulatesa new kind of non-classical learning system with relevance to urban optimization, predictive disaster modeling, and secure communication infrastructure. Using rigorous field-theoreticcalculations, we show how curvature, energy, and quantum state compression can drive intelligent data flow under extreme physical and informational constraints. All derivations, graphs and architecture designs are grounded in first-principles physics, with no experimental data involved. This work offers a new path for theoretical AI systems that are not limited by classical assumptions, proposing a future direction for socially embedded computation based on fundamental physical laws.