Heart disease has long posed health challenges in humans across the globe, predominantly affecting elderly individuals. With evolving lifestyles and dietary preferences, heart disease is now increasingly widespread among younger individuals. The heart is a muscular organ about the size of a fist that functions as the human body’s circulatory pump. Life or death might be determined by cardiovascular health. When individuals learn about heart disease, their first concern is coronary artery disease, which is a heart ailment that can lead to myocardial infarction or cardiac arrest. Blockages in the coronary artery cause myocardial infarction, which varies from 50% to 70% of the cases of valvular heart disease. Stroke, aortic aneurysm, and peripheral artery disease are further serious health issues caused by heart disease. Emerging technologies such as machine learning (ML) algorithms, logistic regression (LR) models, and the Internet of Things (IoT) present promising means of detecting cardiac illness and providing a real-time cardiovascular health monitoring system. Beyond the word heart, cardiovascular diseases (CVDs) are on the rise, particularly in Oman, where they are responsible for 36% of all deaths from noncommunicable diseases. This is brought on by bad habits, inactivity, skipping routine checkups, and not going to the doctor. Monitoring all patients in an accurate manner is often impractical; therefore, it is impossible for practitioners to do this on a continuous basis due to the additional time and expertise required to consult with their patients at least once daily (i.e., 24 hours) after assessing them initially. Our goal in this research is to use the machine learning technique of logistic regression to forecast and account for the patient’s likelihood of having a heart attack.