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Hypersharpening is one of the fusion-based superresolution approaches in remote sensing that improves the spatial and spectral resolution of a hyperspectral image (HSI) with a low spatial resolution. This requirement is achieved by fusing the HSI with a panchromatic image that has high spatial resolution to generate a newly combined variant, which has high spatial quality and high spectral resolution. While several studies in the literature applied neural networks for hypersharpening, there exist unsolved issues such as how to deeply discover the spatial–spectral correlation and inject geometric details without distortion. To address these issues, we propose a hypersharpening technique by applying a multicomponent-based hierarchical fusion network called (HS-McHF), which hierarchically learns the low and high-frequency spatial–spectral features. We then suggest an optimization model to discover the correlation between low-resolution HSI and high-resolution panchromatic images and solve it by stochastic gradient descent through a neural network. Moreover, we decrease the band overlapping in the initial HSI by combining a deconvolution model to prevent spectral distortion and reduce the noise in the panchromatic geometric details injection by deploying an encoder–decoder network. Our extensive experiments demonstrate that the HS-McHF provides superior efficiency compared to state-of-the-art fusion based superresolution approaches.