This manuscript examines the role of GNNs in advancing the field of brain connectomics, with a focus on how these networks can improve our understanding of brain connectivity and its disruptions in neurological disorders. Brain connectomics is the study of the brain’s neural network, which consists of structural, functional, and effective connectivity that can be analyzed using GNNs. These networks provide a sophisticated framework to analyze complex, non-Euclidean data, enabling the exploration of relationships between brain regions and their functions. The manuscript demonstrates that GNNs offer superior performance in tasks like disease diagnosis and cognitive performance prediction. GNNs’ ability to handle large, complex datasets is crucial in brain network analysis, allowing for improved predictions of neurological conditions like Alzheimer’s disease, Parkinson’s disease, and schizophrenia. Additionally, the paper highlights GNNs’ potential to uncover hidden patterns in brain networks, which may lead to earlier detection and personalized treatments for these conditions. Despite the promising potential of GNNs, challenges related to data quality, model interpretability, and computational scalability persist. The manuscript concludes by offering recommendations for future research, such as integrating multimodal neuroimaging data and refining GNN models to better capture temporal dynamics in brain network analysis.