Background <p>Hip fractures in the aged are a major public health concern due to high mortality and poor outcomes. While low ejection fraction (EF) is linked to increased mortality in many populations, its impact on aged hip fracture patients is unclear. This study investigates the relationship between EF and all-cause mortality and develops a predictive model based on EF and other clinical factors.</p> Methods <p>A retrospective cohort study was conducted, including 1,526 aged patients who suffered hip fractures and had EF data recorded. The patients were stratified into three groups based on their EF: normal (EF ≥ 50%), mildly reduced (EF 40%-49%), and severely reduced (EF &lt; 40%). The primary outcome was all-cause mortality, and the secondary outcome was 1-year mortality. Statistical analysis was performed using Kaplan-Meier curves, Cox proportional hazards regression, and multivariate analysis to examine the relationship between EF and mortality. A predictive model for all-cause mortality was developed using multiple clinical factors, and its accuracy was evaluated with the C-index.</p> Results <p>A total of 1,526 aged patients with hip fractures were included in the study, with a mean follow-up of 60 months. Multivariate Cox regression analysis identified nine key predictors for 5-year all-cause mortality: age, EF, triglycerides, coronary artery disease, total cholesterol, diabetes, the E/A ratio, stroke volume, and LVED. EF was found to be an independent predictor of mortality (HR = 0.957, 95% CI: 0.93–0.985, <i>p</i> = 0.003). A nomogram incorporating these variables was developed, enabling individualized risk assessment for predicting 1-year, 3-year, and 5-year mortality. Additionally, a web-based dynamic nomogram was created to enhance accessibility, allowing clinicians to input patient-specific data and obtain real-time survival predictions. The nomogram demonstrated excellent predictive performance, with a C-index of 0.827 and AUCs of 0.840, 0.820, and 0.817 for 1-year, 3-year, and 5-year survival, respectively. Calibration curves showed strong agreement between predicted and observed survival probabilities, while decision curve analysis confirmed the model’s clinical utility in guiding personalized risk management.</p> Conclusion <p>The predictive model developed in this study, based on EF and clinical factors, helps identify aged hip fracture patients at higher risk of mortality, supporting more targeted preoperative and postoperative management.</p> Clinical trial registration <p>Not applicable.</p>