Hyperspectral imaging (HSI) enables a detailed analysis of land cover and materials by capturing extensive spectral information in numerous contiguous bands. While this richness offers high classification potential, it also introduces significant challenges such as high dimensionality, spectral redundancy, and a scarcity of labeled data. Traditional machine learning techniques such as SVM, k-NN, and Random Forests often underperform in this domain due to their limited capacity to incorporate spatial context. Recent developments in deep learning have shown that Convolutional Neural Networks (CNNs) can more effectively model spectral–spatial correlations. However, existing CNN architectures such as 1D, 2D, and even 3D-CNN models trade-offs between spatial and spectral learning, computational complexity. To overcome these limitations, we propose a novel Hybrid Residual 3D CNN with Focal Loss for Hyperspectral Image Classification. The proposed architecture incorporates residual connections to enhance feature learning and gradient flow and utilizes focal loss to mitigate class imbalance during training. Principal Component Analysis (PCA) is applied for dimensionality reduction, preserving the most informative spectral components while reducing computational overhead. Fixed-size 3D patches are extracted to retain localized spatial–spectral context. Experimental results on three benchmark datasets such as Indian Pines, Salinas, and Pavia University demonstrate that the proposed architecture outperforms traditional machine learning approaches and baseline deep learning models in terms of accuracy and robustness compared.