Aerogels are low-density and highly porous materials (90–99% porosity) withexceptional thermal and mechanical properties, governed by their intricatenanoporous microstructure. Understanding their structure-property relationshipsis essential for optimizing their performance across industrial applications. A sig-nificant challenge appears in precisely identifying the complete pore space andthus mapping their microstructural morphology of aerogels. This work presents adeep learning-driven digital twin framework for aerogels, leveraging ConditionalGenerative Adversarial Networks (cGANs) and Convolutional Neural Networks(CNNs) for 3D microstructure reconstruction and predictive modeling. Our ap-proach reconstructs 3D aerogel microstructures from synthetic 2D scanning elec-tron microscopy (SEM) images that mimic real samples by incorporating deptheffects. A CNN predicts key microstructural parameters, including pore radius,relative density, and pore size distribution, with minimal error. A 3D cGAN thengenerates aerogel microstructures by capturing global spatial features and condi-tioning on the extracted parameters.We demonstrate that conditioning improves the fidelity of reconstruction by en-forcing physically meaningful constraints. This method provides a scalable, data-driven approach for microstructure modeling, enabling efficient structure-propertypredictions, and guiding aerogel design for targeted applications.