As a major challenge in the global field of critical care medicine, sepsis is associated with high morbidity and mortality, driving intensive research into the development of precise risk prediction models. Among these, machine learning and deep learning-based predictive models have garnered particular attention. This article systematically reviews the evolution of sepsis risk prediction models and the challenges associated with their clinical translation, with a focus on their clinical applicability in common septic complications, including acute kidney injury, encephalopathy, and coagulation disorders. Current evidence indicates that machine learning models, by integrating multidimensional dynamic data from electronic health records, significantly enhance early warning capabilities and individualized predictive performance. However, major bottlenecks limiting clinical translation include insufficient data standardization, lack of model interpretability, and inadequate external validation. Future research should prioritize multicenter collaboration, time-series modeling strategies, and explainable artificial intelligence frameworks to optimize clinical translation pathways and facilitate the paradigm shift from traditional empirical approaches to data-driven precision strategies in sepsis risk stratification.