The project centers on developing AI-based tools that can estimate the mechanical properties of brain tissue, such as stiffness, by utilizing widely available single-shell diffusion MRI (dMRI) data. The endeavor seeks to provide a more accessible alternative to Magnetic Resonance Elastography (MRE), which is limited by the need for specialized equipment. The project is structured into three essential parts. The first part involves creating synthetic multi-shell dMRI data from single-shell scans using state-of-the-art generative AI techniques. This approach simulates the necessary data for mechanical property estimation without MRE hardware. In the second part, the focus shifts to predicting brain tissue stiffness. Here, deep learning models are trained in a supervised fashion using both synthetic dMRI data and actual tissue stiffness measurements. The final part of the project applies the developed AI tools to the diagnosis and characterization of Parkinson's Disease. By integrating various imaging modalities and employing models that can handle incomplete data, this stage aims to enhance the predictive diagnosis of Parkinson's Disease and potentially other neurological conditions. The successful implementation of this project could transform the neuroimaging approach in clinical practice, making it more feasible for hospitals to assess brain tissue mechanics for diagnosis and treatment planning.