Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, poses a significant challenge to the global healthcare system due to its high morbidity and mortality rates. Fortunately, machine learning algorithms have emerged as powerful tools in analyzing vast and increasingly complex clinical datasets derived from electronic health records, thereby assisting in clinical decision-making. In recent years, machine learning technology has achieved groundbreaking advancements in the field of sepsis, encompassing multiple crucial aspects ranging from early warning, precise diagnosis, personalized medicine, prognosis evaluation, to special management for pediatric and geriatric patients. With the continuous optimization and iterative development of machine learning models, it has introduced unprecedented intelligent solutions to the clinical diagnosis and treatment pathways of sepsis.This review summarizes the current applications and advancements of machine learning in sepsis management, providing valuable references for further related research.