The early prevention of myocardial infarction (MI), a complication of cardiovascular disease (CVD), is an urgent need for the timely provision of medical intervention and the reduction of cardiovascular mortality. The performance of machine learning (ML) has proven useful in aiding the early diagnosis of this disease. In this work, we utilize clinical cardiovascular disease risk factors and biochemical data, employing machine learning models i.e. Random Forest (RF), Extreme Grading Boosting (XGBoost) and Adaptive Boosting (AdaBoost), to predict the 10-year risk of myocardial infarction in patients with 10-years follow-up for CVD. We used the cohort of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, while 3267 patients were included in the analysis (1361 suffered from MI). We calculated the performance of machine learning models, more specifically the mean values of Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) of each model. We also plotted the corresponding receiver operating characteristic curve for each model. The findings of the analysis reveal that the Extreme Gradient Boosting model detects MI with the highest accuracy (74.27 %). Moreover, explainable artificial intelligence was applied, especially the Shapley values were calculated to identify the most important features and interpret the results with XGBoost.