Density Functional Theory (DFT) is the cornerstone of modern quantum simulations but suffers from functional approximations and high computational cost for complex 3D materials. I propose a unified AI-driven quantum framework combining DFT, artificial intelligence (AI) corrections, and machine-learned force fields (MLFFs). This hybrid approach corrects DFT errors using neural networks and generalizes results into efficient MLFFs for large-scale 3D simulations. The framework achieves near ab initio accuracy at reduced computational cost, enabling predictive simulations of crystalline solids, nanostructures, and energy materials. This work represents a paradigm shift in computational chemistry and materials science by unifying physics-based and data-driven models.