Multimodal hashing projects multimodal data into compact binary codes, enabling rapid and storage-efficient retrieval of large-scale multimedia content. In practical scenarios, the issue of missing modality frequently arises when dealing with multimodal data. Existing incomplete multimodal hashing techniques directly recover missing modalities by neural networks, resulting in a disjointed representation space between the recovered and true data. In this paper, we present a novel recovery paradigm, namely Prototype-based Modality Completion Hashing (PMCH). Instead of directly synthesizing it from available modalities, PMCH adaptively aggregates associated within-modality prototypes to recover missing modality data. Specifically, PMCH introduces an within-modality prototype learning module to optimize representative prototypes for each modality. These prototypes act as recovery anchors and reside within the same representation space as their corresponding modality data. Subsequently, PMCH adaptively aggregates the associated within-modality prototypes with coefficients derived from the modality-specific Weight-Net. By utilizing prototypes from the same modality, the semantic disparity between the reconstructed and authentic data can be substantially diminished. Extensive experiments on three widely used benchmark datasets demonstrate that PMCH can effectively recover the missing modality, and attain state-of-the-art performance in both complete and incomplete multimodal retrieval scenarios. Code is available at https://github.com/Sasa77777779/PMCH.git.