Parkinsons disease, PD, is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance imaging or MRI scan. However, symptom variations cause inaccurate assessments, and the analysis of MRI scans requires experienced specialists. This research proposes to accurately diagnose PD severity with deep learning by combining symptoms data and MRI data from the Parkinsons Progression Markers Initiative database. A new hybrid model architecture was implemented to fully utilize both forms of clinical data, and models based on only symptoms and only MRI scans were also developed. The symptoms based model integrates a fully connected deep learning neural network, and the MRI scans and hybrid models integrate transfer learning based convolutional neural networks. Instead of performing only binary classification, all models diagnose patients into five severity categories, with stage zero representing healthy patients and stages four and five representing patients with PD. The symptoms only, MRI scans only, and hybrid models achieved accuracies of 0.77, 0.68, and 0.94, respectively. The hybrid model also had high precision and recall scores of 0.94 and 0.95. Real clinical cases confirm the strong performance of the hybrid, where patients were classified incorrectly with both other models but correctly by the hybrid. It is also consistent across the five severity stages, indicating accurate early detection. This is the first report to combine symptoms data and MRI scans with a machine learning approach on such a large scale.