Heart failure is a serious and end-stage status of various heart diseases, characterized by comparatively high rate of readmission and mortality, and has become an important public health issue. The risk of readmission and mortality following discharge of an index hospitalization are key indicators to evaluate the quality of medical care among patients with acute heart failure. Therefore, it is important to carry out risk prediction research for patients with acute heart failure, quantify the disease risk, perform risk stratification, optimize clinical decision-making, elevate patients' quality of life and prognosis, and comprehensively improve the medical quality of acute heart failure. During the past 20 years, foreign researchers have developed dozens of models to predict the risk of acute heart failure readmission and mortality, and Chinese researchers have also developed up to 10 models applicable to the Chinese population. However, there is no recommended risk prediction model for acute heart failure in current clinical guidelines across China. In this report, we aim to introduce the major models for predicting the risk of acute heart failure readmission and mortality from home and abroad, focus on putting forward limitations of established models, and initiating potential directions for future studies from the following aspects: integrate multi-source data, mine emerging biomarkers, establish polygenic risk scores, optimize machine learning methods, promote flexible adjustment, and broaden approaches that applicable for various scenarios. Accordingly, this study will help facilitate domestic research in predicting the risk of readmission and mortality among patients hospitalized for acute heart failure.