Heart Disease is a very Common Disease in India, the death rate is almost one person per day. The situation seems to be demanding of a system that is going to help in the process of detection so that the problem could be handled in a proper way. We are in search of a system or technique that can reduce the death rate and should be very reliable. In the current time machine learning is the technology for the health sector. We in the current work made an exploration of the machine learning landscape or the support vector machine for handling the problem under consideration. We took experimental analysis data from Kaggle. We performed training using different kernels applicable for support vector machine. In the sections presented in the paper, we will be discussing the Introduction where we will be having a brief overview of the concepts under consideration. Then we will look into the problem statement. After which, there is a brief literature review section where we will acknowledge various works done in the field that are helpful in presenting the current work. We will be having a result and implementation section immediately after which we will be having the conclusion section. In the conclusion, we will observe the comparative analysis of the performances of the various kernels for the cardiovascular disease prediction.