Cardiovascular diseases remain one of the leading causes of sudden mortality worldwide, highlighting the urgent needfor early detection, continuous monitoring, and timely medical intervention. This project presents an AI-powered heart monitoringand disease prediction system that integrates IoT biomedical sensors with advanced machine learning and deeplearningtechniques on a Raspberry Pi platform. Real-time physiological signals—such as ECG readings, heart rate, body temperature, andhumidity—are captured and processed locally on the device. ECG waveform images are analysed using a deep learning-basedConvolutional Neural Network (CNN) to detect cardiac abnormalities and early risk indicators. For structured symptomdatasets,traditional machine learning algorithms including Random Forest are used to classify potential heart diseases andassessuserhealth risk levels. Processed data and real-time analytics are visualized and transmitted to cloud platforms such as ThingSpeakforremote monitoring by clinicians and caregivers. The system also provides instant alerts when abnormal cardiac activityis detected,helping users seek timely medical attention. Designed to be low-cost, portable, and intelligent, this embeddedAI solutiondemonstrates how IoT sensing, deep learning-driven ECG analysis, and cloud-based health monitoring can collectivelyenhancepreventive healthcare especially for home care, elderly patients, and rural or resource-constrained environments. Keyword : AI-powered Healthcare, Heart Disease Prediction, ECG Image Classification, Convolutional Neural Network(CNN),Deep Learning Algorithms, Raspberry Pi, IoT-based Health Monitoring, Biomedical Sensors, ThingSpeak Cloud, Heart RateSensor, ECG Sensor, Predictive Analytics, Remote Healthcare System. Etc