Cardiovascular diseases (CVDs) are a major cause of global mortality, underscoring the need for intelligent and accessible cardiac health monitoring. This paper proposes a non-wearable Internet-of-Medical-Things (IoMT) system combining real-time sensing, edge processing, and AI-driven diagnostics. Stationary sensors MAX30102 (heart rate, SpO2) and AD8232 (ECG) interfaced with micro-controller (ESP8266), processes data locally and feeds into the machine learning models trained on UCI Cleveland dataset. Random Forest and XGBoost achieved over 80% accuracy in predicting early cardiac risk. A Flask-SQLite web application provides role-based doctor/patient access, and a Natural Language Pro-cessing (NLP) based interactive chatbot offers personalized guidance. The system delivers scalable, real-time, edge-enabled cardiac diagnostics without relying on wearable devices.