PURPOSE: Hyperspectral imaging (HSI) collects detailed spectral information across hundreds of narrow bands, providing valuable datasets for applications such as medical diagnostics. However, the large size of HSI datasets, often exceeding several gigabytes, creates significant challenges in data transmission, storage, and processing. This study aims to develop a wavelet-based compression method that addresses these challenges while preserving the integrity and quality of spectral information. APPROACH: The proposed method applies wavelet transforms to the spectral dimension of hyperspectral data in three steps: 1) wavelet transformation for dimensionality reduction, 2) spectral cropping to eliminate low-intensity bands, and 3) scale matching to maintain accurate wavelength mapping. Daubechies wavelets were used to achieve up to 32x compression while ensuring spectral fidelity and spatial feature retention. RESULTS: The wavelet-based method achieved up to 32x compression, corresponding to a 96.88% reduction in data size without significant loss of important data. Unlike PCA and ICA, the method preserved the original wavelength scale, enabling straightforward spectral interpretation. Additionally, the compressed data exhibited minimal loss in spatial features, with improvements in contrast and noise reduction compared to spectral binning. CONCLUSIONS: This study demonstrates that wavelet-based compression is an effective solution for managing large HSI datasets in medical imaging. The method preserves critical spectral and spatial information, and therefore facilitates efficient data storage and processing, providing the way for practical integration of HSI technology in clinical applications.