Over the last two decades, heart disease, also regarded as cardiovascular disease, is the major cause of death worldwide. Conferring to the World Health Organization, over 17.9 million individuals have died every year as a result of coronary heart disease, with coronary stroke responsible for 80% of all fatalities. Deaths in large numbers are frequent in low and middle-income countries. Heart disease is driven by a range of factors, including private and job-related practices, as well as inborn predisposition. Smoking, heavy alcohol and caffeine consumption, stress, and insufficient physical activity, as well as health sedentary lifestyles, hypertension, increased cholesterol levels, and pre-existing heart disorders, are all risk factors for heart disease. The capability to diagnose heart disease early and precisely plays a critical part in taking preventive steps to avoid fatalities. In certain cases, heart disease can be completely cured by a mixture of dietary changes, medicine, and if required surgery. With the proper treatment, the symptoms of heart failure can be reduced and the heart's rhythm can be improved. The estimated outcomes can be used to avoid and therefore reduce the cost of surgical care as well as other costs. My work's ultimate goal would be to reliably forecast using various attributes and come up with a solution that will assist them in avoiding potential losses. To come up with an accurate prediction and observations about the person's well-being, this prediction model will use a data science life cycle, along with machine learning models.