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Dengue fever continues to be a significant public health issue across the globe because it can lead to life-threatening complications. Severity prediction in a timely and precise manner is imperative for proper clinical management and effective resource utilization. Conventional models fail to identify intricate relationships between heterogeneous clinical, demographic, and epidemiological variables. For this purpose, we develop an innovative framework—Graph Neural Network with Attention Mechanism (GNN-AM)—aimed at enhancing dengue severity prediction. In the suggested method, every patient is viewed as a node in a graph with edges indicating clinical similarity in terms of health properties. The incorporation of attention mechanisms enables the model to selectively pay attention to important clinical indicators like fever duration, platelet count, and bleeding tendencies. This selective attentiveness improves prediction quality by giving maximum importance to the most important features while reducing the impact of less significant data. The model was trained and tested on a dataset of laboratory-confirmed dengue cases that contained clinical symptoms, laboratory results, and demographics. Experimental results showed that attention-augmented GNN performed better than both typical GNNs and traditional machine learning models, recording an accuracy of 90.3%, a recall of 88.9%, and an F1-score of 89.6%. Results highlight the efficacy of the GNN-AM framework in classifying dengue severity accurately and the ability to emphasize crucial clinical indicators using attention mechanisms. In the future, this model can be combined with Electronic Health Records (EHRs) and implemented in real-world healthcare environments using federated learning methods to maintain data privacy across institutions.