This paper investigates the correlation between building material properties and indoor network coverage, encompassing both indoor Wi-Fi and outdoor 5G technologies to provide customized network services tailored to users' needs in diverse areas. We first analyze the impact of building material characteristics, with a special focus on wall materials, on the distribution of wireless signal propagation. Then, a ray-tracing-based method is introduced to synthetically generate high-quality training data that covers fine-grained network scenarios with a wide range of wall materials, extending beyond traditional materials. This dataset serves as the foundation for our proposed Global Embedding Isomorphism Network (GemNet), a machine learning framework that facilitates the prediction of optimal material parameters for customized in-building coverage. This innovation enables architects and builders to design novel, network-friendly materials, ensuring ubiquitous and on-demand network services. Extensive evaluations consistently demonstrate a re-markable prediction accuracy of 90.52% on material parameters, underscoring the framework's ability to optimize indoor wireless network planning through the lens of material engineering.