Parkinson’s Disease (PD), an enduring and degenerative disorder of the nervous system, presents significant challenges for timely and accurate diagnosis due to its complex and variable symptomatology. In this study, we propose DeepPark-Net, a unified CNN-BiGRU-MLP framework designed to independently process Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data for multimodal PD detection. Unlike prior studies, we perform evaluations under both subject-dependent (SD) and, for the first time, subject-independent (SI) settings for EEG, demonstrating the model’s robustness to inter-subject variability. Experimental results show that DeepPark-Net achieves 100% accuracy in SD and 86.53% accuracy in SI settings for EEG, and 95.6% accuracy for MRI under SD evaluation. A comparative analysis shows our framework outperforms existing EEG and MRI-based PD detection methods. This work lays a strong foundation for developing robust deep learning (DL) solutions for neurological disease detection using multimodal neuroimaging data. The source codes related to this research are available at: https://github.com/Sankhadip-007/DeepPark-Net-A-Multimodal-Deep-Learning-Framework-for-Parkinson-s-Disease-Detection .