Aim To develop and validate a risk prediction model for fundus arteriosclerosis. Methods Patients without fundus arteriosclerosis who underwent physical examination in Huadong Sanatorium from 2006 to 2013 were selected as the derivation cohort. Lasso method was used to screen the predictors, and Cox regression method was used to establish the prediction model. The model was presented as an online calculator. The Bootstrap method was used for internal validation, and the physical examination subjects in the hospital from 2015 to 2021 were selected for temporal validation. The concordance (C) statistic was used to quantify discrimination, and the calibration of the model was evaluated by comparing the predicted survival probability with the observed survival probability using calibration plots and the Kaplan-Meier method. Results The derivation cohort included 33 218 participants and external validation cohort included 53 863 participants. The final model included nine predictors:age, body mass index, alcohol consumption, diastolic blood pressure, hypertension, fasting blood glucose, diabetes, triglyceride, and serum uric acid. Online calculator site at:https://rui2022.shinyapps.io/DynNomapp/. The C statistic of internal validation was 0.841, and the C statistic of external validation was 0.856. Calibration performed well in both the derivation and external validation cohorts. Conclusion The established risk prediction model of fundus arteriosclerosis has a good predictive ability in the physical examination population. The model only needs the variables routinely obtained by the hospital, so it can be applied to the individualized management of physical examination population and the decision support of the management of high-risk people.