Hepatitis C is a serious and potentially life-threatening liver disease that often remains asymptomatic for years, making early diagnosis challenging. If left undetected, it can lead to severe complications such as cirrhosis, liver failure, and hepatocellular carcinoma. Given the limitations of traditional diagnostic methods, which often struggle with accuracy and reliability, there is a growing need for advanced computational techniques to improve early detection and classification. This study introduces an ensemble machine learning approach to enhance the accuracy of Hepatitis C classification. The proposed method integrates multiple classifiers, including Naïve Bayes, Support Vector Machine, Decision Tree, and Linear Regression, using a majority voting mechanism. By addressing class imbalance and optimizing feature selection, this ensemble approach enhances predictive performance. Experimental evaluations using the Hepatitis UCI dataset demonstrate that the proposed method achieves a remarkable accuracy of 97.5%, outperforming individual models and conventional techniques. These findings underscore the potential of ensemble learning in medical decision support systems, providing a reliable tool for early diagnosis, risk assessment, and timely intervention.