Background and Aims Patients with advanced liver cirrhosis often experience a series of complications, leading to an increased risk of death. Therefore, early identification of high-risk patients for liver cirrhosis mortality is of significant clinical importance. In this study, we used the H2O platform and automated machine learning (AutoML) framework to develop a predictive model for 30-d in-hospital mortality in liver cirrhosis patients, aiming to provide new methods for improving patient prognosis and clinical management of liver cirrhosis.Methods General information and laboratory examination data were collected from hospitalized liver cirrhosis patients at Jintan Hospital affiliated with Jiangsu University and Hunan Provincial People's Hospital. Multiple machine learning algorithm models for mortality outcomes were established using the H2O AutoML framework. ROC curves were plotted, and confusion matrices were used to evaluate the performance of the models. Furthermore, important variables were visualized.Results The best model, gradient boosting machine (GBM), had a Gini value of 0.994, R² of 0.775, and LogLoss of 0.120. Important variables in the model included prothrombin time, creatinine, white blood cells, and age. The SHAP feature graph and partial dependence graph demonstrated the correlation between important variables and the overall predictions of the model. LIME visualization showed the individual predictive effects of the variables. The best GBM model had a specificity of 0.950, sensitivity of 0.676, and AUC of 0.793 in the validation set, outperforming four algorithm models (XGBoost, Logistic regression, random forest, and deep learning), as well as the MELD and ALBI scores.Conclusions The established machine learning model for predicting short-term mortality provides an effective tool for screening the risk of short-term death in patients with liver cirrhosis. However, its reliability still needs further evaluation through external validation from multiple centers.