Cardiovascular disease (CVD) is one of the main diseases in today’s population worldwide the main reason behind this hug break out of the disease is smoking, diabetes and high blood pressure its affect is not limited to the old generation its effects can be seen in younger generation which even worst the health issues. More over the research as shown that the viral infection may also impacted on the cardiovascular health. The viral infection has increase the chance of the heart diseases which lead to poor health and increase the chance of disease that may damage the other organs in a body. The study with the use of Machine Learning with the help of the Random Forest Algorithm develop a predictive model for evaluation of Cardiovascular risk in the patient with the viral infections. The model aim to identify the risk factors with detects the risk of the disease at the early stage. Early detection may help the patient for their proper medication and avoid the change to contaminate with the viral infections which would lead to improving the outcome of the patient health. One of the major challenges while developing the model is the variability of the viral infections. Virus sometime evolve them self over the time which make it difficult to detect it more accurately by the model. The Random Forest algorithm is well suit because of its ability to handle large datasets with multiple independent and dependent variables. It constructs the multiple decision tree and merge them all for the output. The algorithms reduce the risk of overfitting and improve the ability to generalize among the different patient population. Training the model with help of the real time data of various patient with various condition of the viral infection and its causing effect on the cardiovascular will get easy to detect the pattern and help to train the model more accurately.