By the emergence and rapid spread of 2009 pandemic influenza A (H1N1) virus through the world,several methods have been developed to predict and prevent this lethal disease. Although many effortshave been made by statistical and traditional intelligent methods to anticipate this disease, but none ofthem could satisfy the expectations of specialists. This paper aims to present an efficient hybrid methodto predict H1N1 with a reliable sensitivity. In this way, three methods including Gaussian mixture model(GMM), neural network (NN), and fuzzy rule-based system (FRBS) have been fused in order to providean accurate and reliable prediction scheme to anticipate presence of H1N1influenza. In this study, 230individuals were participated and their clinical data were collected. The proposed hybrid scheme wasimplicated and the results showed to be superior to using each of the decision components containingNN, FRBS, and GMM classifiers. The achieved results produced 95.83% sensitivity and 80.95%specificity on unseen data which support the effectiveness of the hybrid method to predict the influenza inits golden time.