Cardiovascular diseases are one amongst the top killers worldwide. The way we live, the food that we eat, metabolic rate, activity level leads to such health issues. Some of these include health factors like obesity, high blood pressure, high cholesterol levels, smoking, diabetes, and age. The food that we eat also plays an important role in heart risk. These days people are preferring spicy, deep fried food, this food in turn causes obesity and high bp leading to heart problems. Stress also plays a vital role in cardiovascular risk. Quick detection of this risk in individuals allows them to take a quick medical action. The standard methods cannot be completely reliable and are slow. To provide an effective solution to this, we used machine learning algorithms. We trained some models like Decision Tree model and Random Forest model on our dataset that includes most of the key factors that lead to this risk. We used feature selection techniques to boost our model's performance and reliability. We used bagging with extra randomness to predict the results. Prevention is always better than cure so early detection is always better than being at risk.