Genetic testing for pathogenic germline variants is critical for the personalized management of high-risk breast cancers, guiding targeted therapies and cascade testing for at-risk families. In this study, MAIGGT (Multimodal Artificial Intelligence Germline Genetic Testing) is proposed, a deep learning framework that integrates histopathological microenvironment features from whole-slide images with clinical phenotypes from electronic health records for precise prescreening of germline BRCA1/2 mutations. Leveraging a multi-scale Transformer-based deep generative architecture, MAIGGT employs a cross-modal latent representation unification mechanism to capture complementary biological insights from multimodal data. MAIGGT is rigorously validated across three independent cohorts and demonstrated robust performance with areas under receiver operating characteristic curves of 0.925 (95% CI 0.868 - 0.982), 0.845 (95% CI 0.779 - 0.911), and 0.833 (0.788 - 0.878), outperforming single-modality models. Mechanistic interpretability analyses revealed that BRCA1/2-mutated associated tumors may exhibit distinct microenvironment patterns, including increased inflammatory cell infiltration, stromal proliferation and necrosis, and nuclear heterogeneity. By bridging digital pathology with clinical phenotypes, MAIGGT establishes a new paradigm for cost-effective, scalable, and biologically interpretable prescreening of hereditary breast cancer, with the potential to significantly improve the accessibility of genetic testing in routine clinical practice.