Knowledge Graph Completion (KGC) remains challenging due to the inherent complexity of capturing both structural patterns and multi-modal information in a unified framework. Existing approaches usually treat entities and relations as simple nodes and edges, limiting their ability to model complex higher-order interactions. Moreover, existing multimodal KGC approaches cannot dynamically adjust to the heterogeneous information quality of different modalities. To address the aforementioned limitations, we propose Hypergraph-Enhanced Multimodal Knowledge Graph Completion(HyperKGC), a novel framework that leverages hypergraph structures and dynamic multimodal fusion for enhanced knowledge graph completion. The proposed method uniquely represents relations as hyperedges and entities as nodes within a hypergraph, enabling more expressive structural tokens that capture higher-order connecting patterns. Furthermore, an adaptive multimodal fusion mechanism is proposed to dynamically calibrate the contribution of visual, textual, and structural information based on their relevance and reliability for each entity. Extensive experiments on benchmark datasets including DB15K, MKG-W, and MKG-Y demonstrate that the proposed HyperKGC consistently outperforms state-of-the-art methods across standard metrics.