Multimodal fusion of color fundus photography (CFP) and optical coherence tomography (OCT) B-scan images has demonstrated superior diagnostic potential for retinal diseases compared to single-modality approaches. However, existing fusion paradigms - whether through naive concatenation or attention mechanisms - treat cross-modal interactions indiscriminately, lacking adaptive modulation of modality-specific contributions under varying clinical scenarios. We propose an adaptive fusion framework that dynamically routes and refines multimodal signals for enhancing disease recognition. The framework comprises two key components: 1) Dynamic Cross-Modal Expert Routing (CMER), which selectively activates convolutional neural network (CNN) experts from one modality based on contextual guidance from the other, ensuring only the most relevant feature extractors contribute to fusion; and 2) Top-K Expert-Guided Wavelet Fusion (TEWF), which performs discrete wavelet transform (DWT) to decompose selected features into low- and high-frequency subbands. Cross-modal attention is then applied specifically to high-frequency components, where lesion-specific microstructures reside, enabling frequency-aware fusion. Finally, inverse DWT (IDWT) reconstructs the fused representation, weighted by CMER-derived importance scores to amplify informative modality cues while suppressing redundancy. Experimental validation on two multimodal retinal datasets demonstrates that our method achieves state-of-the-art performance, outperforming existing fusion strategies by significant margins in disease classification accuracy and robustness.