So far, the specific case of COVID-19 post-vaccination adverse events pertaining to persons with cardiovascular risk factors and comorbidities has not been explored empirically, which limits the understanding of the underlying causes of adverse reactions to vaccination by this category of persons. This paper explored Explainable AI (XAI) to identify the critical determinants of post-vaccination mortality in persons with cardiovascular risk factors. To do this, we extracted 16657 records of persons with cardiovascular risk factors from the VAERS open dataset (from 2020 to May 2024). We then employed predictive modelling using a process that involved four stages. The experimental process involved extracting relevant data from VAERS and data preprocessing,  conducting comparative performance evaluation of seven machine learning (ML) algorithms. We also compared the performance of two stacked ensemble models composed of six base models, using Catboost and XGBoost as the meta-learners in each case. Thereafter SHAPley Additive Explanations (SHAP) was used to interpret the predictions of the best-performing model. The result showed that CatBoost had the best performance among the base ML models (Acc = 0.96, F1=0.96, AUC = 0.96), while Stacked ensemble - XGBoost had the best overall performance (Acc = 0.96, F1=0.96, AUC = 0.99). Also, we found the important predictors of post-vaccination mortality in persons with cardiovascular comorbidity. Generally, older age, a higher number of days spent in the hospital increases the risk of mortality, while the absence of current illness, life-threatening condition, hospitalization, prolonged hospitalization, disability, birth defect, doctor visit, and emergency care; and vaccination dose completion will enhance the probability of survival. However, the presence of diabetes, high cholesterol, high blood pressure, and other illnesses increases the risk of mortality.