This paper presents a human-centered intelligent heart disease prediction and AI-powered recommendation system designed for early risk detection and preventive healthcare. The proposed system continuously monitors key physiological parameters including Blood Oxygen Saturation (SpO₂), Heart Rate (BPM), and Body Temperature using IoT sensors integrated with a Node MCU ESP32 microcontroller. Data collected from MAX30102 and MLX90614 sensors is transmitted to a Firebase Realtime Database through the Arduino environment. An Android application applies a Decision Tree machine learning algorithm to classify heart attack risk into No Risk, Medium Risk, and High Risk categories. Upon detecting abnormal conditions, personalized health recommendations and medical alerts are generated to support timely intervention. The system ensures real-time monitoring, privacy-aware analysis, and remote health tracking, promoting proactive cardiovascular care and reducing healthcare burden through continuous intelligent monitoring.