Due to the inadequacy in utilizing complementary information from different modalities and the biased estimation of degraded parameters, the unsupervised hyperspectral super-resolution algorithm suffers from low precision and limited applicability. To address this issue, this paper proposes an approach for hyperspectral image super-resolution, namely, the Unsupervised Multimodal Multilevel Feature Fusion network (UMMFF). The proposed approach employs a gated cross-retention module to learn shared patterns among different modalities. This module effectively eliminates the intermodal differences while preserving spatial–spectral correlations, thereby facilitating information interaction. A multilevel spatial–channel attention and parallel fusion decoder are constructed to extract features at three levels (low, medium, and high), enriching the information of the multimodal images. Additionally, an independent prior-based implicit neural representation blind estimation network is designed to accurately estimate the degraded parameters. The utilization of UMMFF on the “Washington DC”, Salinas, and Botswana datasets exhibited a superior performance compared to existing state-of-the-art methods in terms of primary performance metrics such as PSNR and ERGAS, and the PSNR values improved by 18.03%, 8.55%, and 5.70%, respectively, while the ERGAS values decreased by 50.00%, 75.39%, and 53.27%, respectively. The experimental results indicate that UMMFF demonstrates excellent algorithm adaptability, resulting in high-precision reconstruction outcomes.