The accurate prediction and diagnosis of heart disease in the field of medicine pose significant challenges, with cardiovascular disease being a major concern in health services. Within the expanding landscape of healthcare organizations, costly surgeries are increasingly being offered to patients. Despite advancements in medicine, the prevalence of heart disease continues to rise exponentially. Lifestyle factors such as poor diet, alcohol consumption, lack of exercise, and tobacco use are identified as the main contributors to these diseases. Therefore, the implementation of a cloud-based framework (CBF) for health information monitoring and efficient prediction is essential. Recent advancements in machine learning have shown promise in addressing these issues. This proposed system incorporates four steps within the cloud-based platform to enhance the prediction of patients’ health information. Two machine learning methods are utilized to detect and classify heart disease, followed by an evaluation of their accuracy using specific criteria.