Robust computational frameworks capable of integrating complicated, multi-source data are required for the development of personalized diagnosis and treatments for neurological illnesses like Alzheimer’s, Parkinson’s, and Epilepsy. This chapter presents a graph-based approach that uses multi-modal neuroimaging data and clinical parameters to make reliable patient-specific predictions for illness diagnosis and treatment optimization. This approach overcomes the constraints of incorporating patient heterogeneity and high dimensionality by merging brain network studies with sophisticated Graph Neural Networks (GNNs). In this paradigm, neurological examination, cognitive tests, genetic biomarkers, functional MRI (fMRI), structural MRI (sMRI), and diffusion tensor imaging (DTI) are used. To improve data representation in Alzheimer’s Disease (AD), Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) data from the Imaging Studies is used to maintain diversity and reliability. The key innovations include the application of Graph Convolutional Networks (GCNs) for brain connectivity mapping, Graph Attention Networks (GATs) for node-specific feature enhancement. The framework shows outstanding performance; in accuracy and precision, it outperforms traditional deep learning methods in classification of AD, with performance improvement much. The model also identifies individualized biomarkers, including specific cortical regions of importance during disease progression that advance the development of personalized therapeutic targets. These results reinforce the framework’s capacity to yield patient-specific insights informing clinical decision-making and treatment outcomes. This chapter provides a pioneering framework for individualized neurological diagnostics and treatment strategies by combining multi-modal brain network analysis with the latest GNN techniques. It combines computational graph theory with clinical neuroscience to accurately identify biomarkers and improve the translational research. Such advancements present robust opportunities to develop precision.