Sepsis is a medical emergency triggered by pathogenic microorganisms such as bacteria， which can be life-threatening when severe， making early diagnosis and timely treatment crucial. In recent years， machine learning technology has shown tremendous potential in early prediction and treatment strategies for sepsis. By integrating data from multiple sources， machine learning models can assess patient risk accurately and identify high-risk individuals automatically， enabling early diagnosis of sepsis. In addition， machine learning can also assist physicians in developing personalized treatment plans. However， clinical applications based on machine learning methods still face a series of challenges， such as data standardization， model interpretability， and acceptance by medical personnel. Therefore， a comprehensive review was conducted on machine learning based sepsis prediction and intervention decision-making methods. Firstly， the basic process and framework of sepsis prediction and intervention decision-making were introduced. Then， the methods， relevant data and evaluation indicators of sepsis prediction and intervention decision-making were summed up systematically. Furthermore， a detailed summary of the specific applications of machine learning methods in sepsis-related clinical aspects was provided. Finally， the main challenges faced in this field currently were summarized， and future development trends were prospected.