The cardiovascular diseases continue to be among the top causes of death in the world, which has necessitated a high demand of constant and efficient health monitoring systems. The proposed research designates a smart wearable e-health monitoring system based on IoT as a device that provides real time readings of physiological parameters and immediate detection of possible heart abnormalities in order to provide heart patients with proper treatment. The system is proposed to combine wearable sensors and Internet of Things (IoT) to gather and exchange patient health information, such as heart rate, blood pressure, electrocardiogram (ECG), body temperature, and oxygen saturation levels. The gathered data is sent to a cloud-based server in which machine learning algorithms are used to define the health condition and predict risk. The analysis of patient health data using four machine learning algorithms was used to detect abnormal cardiovascular patterns among the patients, namely, the support of the Support Vector Machine (SVM), the Random Forest, the K-Nearest Neighbour (KNN), and the Logistic Regression. The experimental test showed that the given system is accurately able to identify health risks associated with the heart. The algorithm model that had the highest performance was the Random Forest algorithm with an accuracy of 96.2, precision of 95.4 and a recall of 94.8 and the SVM with an accuracy of 93.4. The system also produced health alerts with an average response time of 2-3 seconds, thus allowing quick intervention by the medical personnel. The findings suggest that the suggested IoT-based monitoring system can greatly enhance the provision of remote health care, early diagnosis of heart diseases, and the safety of patients by providing them with constant health monitoring.