In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k-means clustering and improved ensemble-driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f-measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k-means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree-based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.