Accurately predicting subject status from brain network data is a complex task that requires advanced machine learning techniques. In this work, we propose a comprehensive methodology and pipeline for applying supervised graph learning models, specifically Graph Neural Networks, to this task using brain network information derived from diffusion tensor imaging, gray matter and resting-state functional MRI adjacency matrices. Our approach includes a graph pruning step to retain the most relevant edges while preserving crucial information, the generation of node features to enhance graph representations, the creation of synthetic data to balance the dataset and improve training, and the design and training of GNN models for both multi-class and binary classification tasks. Experimental results in a cohort of people with multiple sclerosis and healthy volunteers demonstrate that our methodology effectively captures meaningful patterns in brain graphs, leading to improved classification performance.