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Multimodal survival prediction is crucial for personalized oncology. However, existing methods typically integrate only Formalin-Fixed Paraffin-Embedded (FFPE) slides with a single omics type, such as genomics, overlooking Fresh Frozen (FF) slides that better preserve molecular information, as well as richer multi-omics data like proteomics and transcriptomics. More critically, the complete absence of certain modalities due to clinical constraints (e.g., time or cost) severely limits the applicability of conventional fusion models that rely on inter-modality correlations. To address these gaps, we propose M³Surv, a framework designed to integrate multi-pathology slides (both FF and FFPE) with multi-omics profiles. For multi-slide fusion, we design a divide-and-conquer hypergraph learning approach to capture both intra-slide higher-order cellular structures and inter-slide relationships, yielding a unified pathology representation. To enrich the biological context, we integrate multi-omics data and employ interactive cross-attention to fuse the pathological and omics modalities. To tackle the missing modality, we introduce a prototype-based memory bank. During training, this memory bank learns and stores representative pathology-omics feature prototypes. At inference, even if a modality is entirely missing, the model can query the bank with available features and robustly impute information from the most similar prototype. Extensive experiments on five TCGA cancer datasets and an in-house dataset demonstrate that M³Surv outperforms state-of-the-art methods, achieving an average 2.2% improvement in C-Index. The framework also shows strong stability across various missing modality scenarios, highlighting its clinical potential in real-world, data-incomplete scenarios.