Hyperspectral imaging, with its high spectral resolution, provides valuable information for land cover classification and remote sensing applications. Leveraging this potential, we present a novel approach to land cover classification in hyperspectral imaging (HSI) through spectral-spatial fusion using deep learning techniques. This research integrates Singular Value Decomposition (SVD) as a preprocessing step to enhance spectral information and employs a custom 3D Convolutional Neural Network (CNN) model for spatial feature extraction. The proposed work effectively combines spectral and spatial characteristics, addressing the unique challenges posed by hyperspectral data. SVD, as a dimensionality reduction technique, optimizes spectral information for efficient processing. The 3D CNN model captures spatial patterns and dependencies, enabling improved land cover classification accuracy. We indicated the effectiveness of the spectral-spatial fusion technique on a diverse hyperspectral dataset, achieving considerable improvements in land cover classification accuracy compared to traditional methods. The fusion technique not only enhances the classification performance but also provides understandable features for remote sensing applications.