The early identification of neurodegenerative diseases, like Alzheimer’s and Parkinson’s disease, is crucial for their management, although difficult due to subtle, heterogeneous, and evolutionarily conditioned clinical patterns. In this paper, we present a fuzzy-neural hybrid that incorporates the beneficial properties of fuzzy inference systems and temporal deep learning toward improved early diagnosis. Our model analyzes longitudinal multimodal medical data (neuroimaging and clinical scores), combining fuzzy rule-based feature encoding with Long Short-Term Memory (LSTM) networks for temporal pattern extraction. Interpretability is provided by assigning Gaussian membership functions, while the LSTM component encodes the dynamics of disease progression. The proposed system is validated on synthetic and benchmark datasets, showing robust classification performance and excellent temporal tracking of patient trajectories. Interpretations of internal representations are provided through visualizations such as confusion matrices, t-SNE embeddings, and 3D PCA trajectories. The results indicate that the hybrid approach provides both powerful predictions and transparent operation, making it an excellent option for clinical decision support in neurodegenerative diagnostics. The authors propose a hybrid diagnostic system that uses Gaussian fuzzy membership functions for interpretable feature encoding and LSTM networks for temporal sequence modeling of multimodal medical data. The authors validated their method with synthetic data as well as benchmark datasets, achieving perfect binary classification in their experimental results.