Hepatitis, a severe and highly impactful disease, poses significant challenges for healthcare systems, including limited diagnostic resources, delayed detection, and inadequate treatment infrastructure. This work addresses these issues by developing a machine-learning predictive system to classify hepatitis severity. By employing Logistic Regression, Random Forest, SVM, KNN, and ensemble techniques such as AdaBoost, CatBoost, and Gradient Boosting, the system enhances early detection and severity assessment. The issue of class imbalance was addressed using ADASYN and SMOTE methods applied to two separate datasets. For Dataset 1, following the use of the ADASYN technique, the achieved accuracies were 88.11% for Logistic Regression, 98.92% for Random Forest, 97.30% for AdaBoost, and 96.22% for Gradient Boosting. When SMOTE was employed on Dataset 1, Random Forest and Gradient Boosting reached accuracies of 98.38% and 96.76%, respectively. In the case of Dataset 2, AdaBoost achieved an accuracy of 93.75% after applying both ADASYN and SMOTE. These models analyze clinical data to deliver accurate, timely predictions, reducing the burden on resource-constrained healthcare systems. Ensemble methods enhance model robustness and accuracy, supporting improved decision-making and efficient resource allocation. Furthermore, SHAP offers global explanations of feature importance and force plots for local interpretations, while LIME increases the interpretability of results from black-box models, facilitating effective hepatitis management. Future work will focus on integrating interoperability standards, such as HL7 FHIR, to enable real-time data exchange, facilitating seamless risk assessment and clinical decision support within healthcare workflows.