The liver is one of the most essential internal organs in the human body, acting as a metabolic powerhouse and playing a key role in the immune system. However, Liver Diseases (LD) are rising globally, driven by unhealthy lifestyles and excessive alcohol use. Liver diseases cause millions of deaths annually all over the world, highlighting the need for early diagnosis. This study aims to evaluate the implication of ensemble machine learning techniques—bagging and boosting—for liver disease prediction, utilizing 30,691 instances and 11 features of the Liver Disease Patient Dataset (LDPD). To improve model performance, hyperparameter tuning, outlier removal, normalization for data scaling and feature importance to identify the most significant predictors are used. Eight state-of-the-art ensemble models were evaluated in this study: Random Forest (RF), Extra Trees Classifier (ETC), Bagged Decision Tree (Bagged DT), Adaptive Boosting (AdaBoost), Gradient Boosting (GradientBoost), Xtreme Gradient Boosting (XGBoost), Categorial Boosting (CatBoost) and Light Gradient Boosting Machine (LightGBM). Our experimental results showed that RF algorithm outperformed other algorithms, achieving the highest accuracy, specificity, precision, and F1-score of 99.85%, 99.85%, 99.93% and 99.89% respectively. While LightGBM attained the highest recall rate of 99.86% making it particularly suitable for identifying true positive cases and minimizing the missed diagnosis. These findings highlight the effectiveness of ensemble learning methods (bagging and boosting algorithms) in accurately predicting liver disease.