Multimodal Knowledge Graph Completion (MMKGC) enhances knowledge graphs by integrating various data modalities (text, images, audio, etc.) to improve link prediction, entity classification, and fact inference. Current approaches focus on adapting traditional static graph methods and exploring how to incorporate multimodal data effectively. However, these methods lack comprehensiveness, as conventional KG models are not complex enough to capture the rich information in MMKGs and fail to filter out noise from modality diversity. To address these challenges, we propose MMQuat, leveraging the Kolmogorov-Arnold Network (KAN) for efficient multimodal fusion and faster convergence. We then apply Fourier Transform-based feature extraction to eliminate redundant information and enhance entity alignment by filtering modality-specific noise. Operating in quaternion space, MMQuat exploits hyper-complex operations to model intricate relational patterns. Evaluated on three benchmarks against 16 baselines, MMQuat consistently achieves strong performance while maintaining efficiency and generalizability.