This presentation discusses the development and implementation of an AI-driven computational framework for simulating the mechanical behavior and design of materials with complex microstructures. In the first part of the presentation, we present a geometry reconstruction algorithm utilizing virtual packing and optimization techniques for synthesizing heterogeneous material microstructures. Additionally, an AI-based approach relying on a Deep Convolutional Degenerative Adversarial Network (DCGAN) is developed for the virtual reconstruction of digital twins of human vertebra. To simulate the mechanical behavior of resulting microstructural models, finite element (FE) meshes are generated using the Conforming to Interface Structured Adaptive Mesh Refinement (CISAMR), which is a non-iterative algorithm that transforms an initial structured grid into a high-quality conforming mesh. In the second part of the presentation, we show how these microstructure reconstruction and meshing algorithms serve as a powerful engine for generating the training data for AL/ML applications aimed at predicting the performance of materials/structures. In the first example, we show how a CNN-based model can be trained with the data generated using this framework to predict the failure response of steel pipes subjected to pitting corrosion. We also introduce a new AI-based technique, named Deep Learning-Driven Domain Decomposition (DLD3) method, that can be used as a surrogate for FE analysis for a wide array of problems. Unlike pure scientific AI/ML models, this patented algorithm is highly generalizable and can predict the deformation response of problems with arbitrary geometries and loading. Moreover, compared to FEM, it significantly reduces the operational and computational by obviating the complexity of the modeling process (e.g., no need for mesh generation) and reducing the simulation time.