Voxel-level modeling of resting-state fMRI (rs-fMRI) for Parkinson’s disease (PD) diagnostic classification is challenging due to high dimensionality, scanner variability, and limited sample sizes. We propose a spatio-temporal graph convolutional network (ST-GCN) operating directly on voxel-wise BOLD time series, combining voxel-level harmonization with population-derived functional adjacency to preserve temporal dynamics and spatial structure. The framework was evaluated on a local clinical cohort (BIO), augmented with healthy controls from the Parkinson’s Progression Markers Initiative (PPMI), achieving an AUC–ROC of 0.86 for PD diagnostic classification. Edge-level interpretability analysis revealed stable discriminative patterns involving basal ganglia–thalamo–cortical circuits and associative cortical regions. Given the modest sample size and BIO-only hold-out evaluation, these findings should be interpreted as proof-of-concept methodological evidence rather than as a clinically deployable diagnostic system.