This paper suggests a multimodal fusion learning approach to diagnose neurological diseases through improved medical imaging, hierarchical representations, data augmentation, clinical needs, and feature extractionFeature extraction. A stacked generative adversarial networkGenerative Adversarial Network (GAN) is combined with a variational autoencoderVariational Autoencoder (VAE) to benefit from the advantages of both algorithms, with an application to neurodegenerative disease diagnosis. The integrated method improves the detection of rare neurological disorders by strengthening the assessment algorithms’ robustness and dependability. According to analytical results, the proposed approach performs better than single-modal designs, proving the usefulness of multimodal fusion of images and feature extractionFeature extraction. When applied to an Alzheimer’s datasetDataset, the combined algorithm performs significantly better than traditional methods, achieving 92.5, 88.5, and \(85.6\%\) accuracy in the classification, segmentation, and regression tasks, compared to 85.1, 82.1, and \(80.2\%\) accuracy for a traditional convolutional neural networkConvolutional neural network, traditional U-Net network developed for biomedical images, and linear regression, respectively. When the approach is further applied to a Parkinson’s datasetDataset, we obtain \(90.1\%\) higher classification accuracy outperforming traditional convolutional neural networkConvolutional neural network  \((83.2\%)\) , \(86.5\%\) higher dice coefficient in the segmentation task outperforming traditional U-Net \((80.8\%)\) , and \(86.1\%\) higher R-squared in the regression task outperforming traditional Linear regression \((81.5\%)\) . Finally, we demonstrate how multimodal fusion strategies might bridge the knowledge gap between neurology research and cutting-edge deep learningDeep learning methods by improving clinical decision-making and expanding the understanding of the structural and functional patterns of brain diseases.