Background: Previous efforts in predicting cardiotoxicity risk among patients with breast cancer have mostly focused on preventing and monitoring of cardiovascular complications during cancer therapy. We aimed to develop prediction models for the risk of chronic cardiovascular complications in breast cancer survivors during long-term follow-up. Methods: We used the Korean National Health Insurance Service databases between 2005 and 2021, including 5,131 5-year female breast cancer survivors diagnosed in 2006. The study cohort was randomized on a 4:1 ratio into the derivation and validation cohort. The primary outcome was the occurrence of major adverse cardiovascular events (MACEs), a composite of acute myocardial infarction, congestive heart failure, and stroke, at any time before the final follow-up at 10 years. We used the Cox proportional hazards model (CoxPH) with the least absolute shrinkage and selection operator penalty to determine the order of clinical factors based on their absolute coefficient value. We developed a deep learning survival model (DeepSurv) and compared its performance with traditional models such as CoxPH and random survival forest (RSF) using the same dataset. Model performance was assessed by discrimination and calibration. Results: During the 48054.1 person-years of follow-up, 325 (6.3%) patients developed MACE. We identified 18 relevant clinical factors without zero coefficients, including age, hypertension, atrial fibrillation, stroke, diabetes mellitus, myocardial infarction, household income, congestive heart failure, hemoglobin level, systolic blood pressure, peripheral arterial occlusive disease, chronic kidney disease, aromatase inhibitor use, alcohol consumption, cigarette smoking, tamoxifen use, radiotherapy, and total cholesterol level. The CoxPH, RSF, and DeepSurv model yielded time-dependent concordance index values of 0.713, 0.721, and 0.729, respectively, in the validation cohort. all models demonstrated good integrated Brier scores of 0.031 or less. Conclusions: We developed and validated a deep learning survival model that predict MACEs in individual 5-year breast cancer survivors, incorporating both conventional and breast cancer treatment-related cardiovascular risk factors, and demonstrated good calibration and discrimination. These models can assist breast cancer survivors and clinicians in optimally selecting risk-reducing strategies based on individual MACEs risk.