Multimodal knowledge graphs (MMKGs) integrate structural, visual, and textual modalities to enhance entity and relation representations. However, existing MMKGs completion methods often rely on static fusion strategies that overlook context-specific modality relevance, and they tend to underutilize structural information encoded in the graph topology. In this paper, we present SDMF-MKG, a structure-aware dynamic fusion framework designed to address modality bias and structural underrepresentation in MMKGs. The model incorporates three key components: a structure-guided semantic encoder that preserves topological signals, a dynamic weighting mechanism that adaptively calibrates modality contributions based on triple context, and a KL-regularized loss to encourage balanced modality utilization. We evaluate SDMF-MKG on four benchmark datasets spanning both multimodal-rich and structure-only settings. The model achieves state-of-the-art or competitive performance across most metrics, with notable gains on multimodal datasets such as VTKG-C. Ablation studies further confirm the complementary effects of structure awareness, adaptive fusion, and modality balancing.