<p>Ensuring patient safety during blood extraction and transfusion remains a critical challenge, especially in resource-constrained healthcare settings. This study addresses the need for an intelligent, cost-effective solution by proposing a novel artificial intelligence and Internet of Things-based system for safer blood-related medical procedures and accurate cardiovascular risk prediction. The primary objective is to design and implement a real-time vital monitoring system that continuously tracks key physiological parameters like body temperature, oxygen saturation, and heart rate. The collected data is then utilized to predict heart attack risk using a boosted stacking ensemble model, enhancing early diagnosis and preventive care. The system is built using low-cost, scalable hardware components and tested for real-time accuracy and reliability. Experimental results illustrate the effectiveness of the proposed model in both accurate vital monitoring and high-precision heart attack risk prediction. The integrated approach offers a significant step forward in improving patient safety and proactive healthcare delivery, particularly in low-resource environments.</p>