This research investigates the integration of neutrosophic logic with Graph Neural Networks (GNNs) for disease transmission risk prediction. Neutrosophic Graph Neural Networks (NGNNs) extend the capabilities of Fuzzy Graph Neural Networks (F-GNNs) as utilized in this paper to process structured information. By merging neutrosophic logic with GNNs, the technique significantly improved the handling of uncertainty while monitoring contagious diseases. The standard GNN model fails to process unreliable and unspecific information commonly found in epidemiological data collections. The neutrosophic triplets \((\mathcal {T, I, F})\) within NGNNs represent uncertain components found in disease outbreak conditions. Improved message passage through the NGNN works best while processing complex and extended datasets that programmers structure with Python. The risk evaluation and transmission uncertainty of diseases appear in visual heat maps to assist in the analysis of network dynamics. Through visual output, the health sector receives essential information related to outbreak development for making better decisions. By uniting state-of-the-art computing solutions with health-related implementation, this approach establishes superior standards for medical risk evaluation and produces foundational capabilities for future research. The authors established a firm foundation for advanced epidemic simulation and risk assessment through their work, which integrates theoretical graph theory advances into practical public health scenarios.