It has been demonstrated that cutting-edge technologies like machine learning and big data analytics offer promising answers to issues facing the biomedical community, healthcare, and patient care. By accurately interpreting medical data, they also aid in the early prediction of disease. Early illness detection and symptom identification can further improve disease management tactics. Furthermore, this early diagnosis can help with both the appropriate management of the disease and the management of its symptoms. By creating categorization models, machine learning techniques can be applied to the prediction of chronic illnesses including renal and heart ailments. In 1948, the Framingham Heart Study was started, and it yielded a wealth of information about cardiovascular risk factors. This “big” data is analyzed, and machine learning models are used to forecast an individual's risk of heart disease. These models give precise risk assessments that support early prevention and intervention by taking into account variables like age, gender, blood pressure, cholesterol levels, smoking behaviors, and more. This methodology improves the accuracy of the prognosis for cardiovascular illness and facilitates tailored healthcare advice. The heart disease dataset from “Framingham” comprises more than 4,240 records with 16 columns, encompassing 15 attributes. The research aims to predict whether patients are at a 10-year risk of developing coronary heart disease (CHD).