This paper provides a comprehensive review of the utilization of a hybrid ensemble quantum infrastructure when combined with classical machine learning approaches such as KNN, SVM, RFC, and ANN. The main objective is to assess the effectiveness and performance of quantum-inspired techniques in comparison to traditional approaches. The research study uses both a quantum-inspired and traditional approach to assess liver disease prediction using the UCI machine learning repository dataset. It evaluates and compares the approach’s scalability, computational effectiveness, and prediction accuracy. The study also investigates the accuracy and efficiency benefits of quantum computing over traditional techniques, and the results aim to provide insights on the viability and usefulness of the ensemble approach with quantum infrastructure for predictive modeling in the healthcare industry.