Designing porous materials with specific properties requires advanced inverse design methods to handle the relationships between microstructure and material behavior. This thesis presents a conditional variational autoencoder (CVAE) model that learns these relationships using Minkowski functionals, geometric descriptors including porosity, surface area, and Euler characteristic, as conditioning inputs. By using the CVAE with convolutional neural networks (CNNs), the model captures morphological features of 2D porous microstructures and links them to the target Minkowski descriptors. A Statistical Representative Elementary Volume (SREV) approach is adopted in data preparation to ensure the descriptors are representative of bulk material behavior, allowing the model to account for natural variability in microstructures. The trained CNN-CVAE can generate realistic porous microstructures that meet specified descriptor targets, effectively performing inverse design. Key findings demonstrate that the model reproduces target Minkowski metrics with high fidelity and maintains plausible connectivity and pore structure. Notably, the CVAE exhibits strong generalization, accurately generating microstructures for descriptor combinations not seen during training. These results highlight the potential of Minkowski functionals as effective design descriptors in machine-learning-driven material design. In addition, they confirm that the CVAE is capable of generating and tailoring porous structures to meet targeted descriptor requirements.