Parkinson’s Disease (PD) is a movement-related disease characterized by the decline of dopamine-producing neurons in substantia nigra brain regions, which causes problems with movement control. Machine learning with neuroimaging techniques, especially Magnetic Resonance Imaging, provides a non-invasive automated approach for the diagnosis of PD, enabling both classification and identification of affected brain regions. The present work proposes a Multi-Regions-of-Interest ensemble network ( \(EnsembleRegNet\) ), a decision fusion approach that aggregates the predictive powers of different brain regions. By capturing complementary information from different regions and assimilating the subtle differences across regions, the model enhances the classification of PD. Under \(EnsembleRegNet\) framework, we proposed three ensemble models, wherein two utilise clustering followed by majority voting, while one uses a neural network-based ensemble model (Neural \(EnsembleRegNet\) ). The performance is evaluated on seven large, age- and gender-matched balanced datasets, derived from multiple publicly available datasets and stratified based on parameters such as gender, disease severity, and scanner strength. All three proposed ensemble methods demonstrated better performance than decision models trained on individual brain regions across all datasets. Among ensemble models, Neural \(EnsembleRegNet\)   model outperformed for all datasets except one. The highest Area Under Curve (AUC) value of 83.0% is observed on the Male dataset using Neural \(EnsembleRegNet\)   model. Also, an AUC of \(72.9\%\) is observed for the early-stage PD dataset ( \(HnY = 1\) ). Furthermore, the proposed \(EnsembleRegNet\) framework is designed to identify imaging biomarkers, and the significant biomarkers it uncovers are consistent with the findings reported in the existing literature.