This study focuses on the early identification and precision prevention of acute kidney injury (AKI) following interventional therapy in patients with liver cancer. A prospective, phased mixed-methods research design will be adopted. In the first phase, a prospective cohort study will be conducted to systematically collect clinical and nursing-related data. A Super Learner ensemble algorithm will be employed to develop and validate an AKI risk prediction model, enabling precise risk stratification of patients. In the second phase, a prospective randomized controlled trial will be implemented. Guided by the Health Belief Model (HBM), a risk-stratified proactive nursing intervention will be delivered, and its effectiveness will be compared with that of routine nursing care in reducing the incidence of AKI, improving adherence to hydration protocols, optimizing the management of nephrotoxic medications, and enhancing patients' self-management capabilities. By integrating intelligent risk prediction with proactive nursing intervention strategies, this study aims to establish a reproducible and scalable precision prevention and management model for AKI in liver cancer patients undergoing interventional therapy. The findings are expected to provide scientific evidence for early AKI warning and proactive nursing practice, thereby promoting the precision, intelligence, and standardization of oncologic interventional nursing care.