A system needs to identify and respond to cardiac arrest occurrences right away since fast intervention during the initial few minutes plays an essential role in avoiding permanent damage and improving patient survival. An innovative approach for emergency response enhancement combines pulse sensors with Arduino hardware and sophisticated algorithms in this research paper. A continuous process of patient vital sign monitoring occurs through an Arduino microcontroller which connects to a pulse sensor. The system starts automated responses when it detects a vital drop in pulse rate which signifies cardiac arrest. The system transmits a warning signal to activate a central control center leading to automated AED unit deployment toward the patient. At the same time the system delivers notice to family members and triggers an emergency ambulance request to the closest hospital facility. The Rapidly-exploring Random Tree Star (RRT*) algorithm generates optimal flight paths for the unit through obstacle avoidance and minimized travel time during safe navigation operations. Predictions of cardiac arrest now rely on the Random Forest model which analyzes heart rate alongside ECG and oxygen and blood pressure measurements to enhance previous logistic regression outcomes. The integrated system uses both improved algorithms with coordinated technology in order to respond swiftly to cardiac arrest incidents while boosting survival possibilities for patients.