To enhance patient safety and healthcare outcomes, an IoT-based health monitoring system is proposed, integrating cloud computing, microprocessor-based computing, and advanced sensor technologies. Real-time data, including heart rate and temperature, is collected through sensors and processed locally by a microcontroller utilizing a lightweight machine learning model to detect health irregularities. This local processing ensures timely identification of potential health risks and immediate notification to family members, even in areas with unreliable internet connectivity, thereby enhancing system resilience for emergency response and continuous care. Simultaneously, patient data is securely transmitted to the cloud, where it undergoes advanced analysis and long-term trend monitoring. Upon detecting abnormalities, caregivers are alerted, and access to real-time and historical health patterns is provided through an intuitive interface, facilitating informed decision-making. The integration of local processing with cloud-based analytics effectively addresses challenges related to connectivity limitations and microprocessor resource constraints, ensuring a scalable, flexible, and reliable solution for patient monitoring. Additionally, critical issues such as data security, power consumption, and the optimization of machine learning models for microprocessors are examined, emphasizing the necessity for further research and development in this domain.