Cardiac arrhythmias are a major cause of morbidity and mortality worldwide. Early detection and treatment of cardiac arrhythmias can significantly improve patient outcomes. However, current monitoring and detection systems are limited by the poor signal quality, limited spatial resolution, and high costs. In this paper, we propose a smart biomedical sensor network for multi-patient cardiac arrhythmia detection. The system consists of wearable sensors that collect electrocardiogram (ECG) and other physiological data, a wireless network that transmits the data to a central monitoring hub, and a cloud-based algorithm that analyzes the data to detect cardiac arrhythmias. The system is designed to be scalable and can monitor multiple patients simultaneously. We evaluate the system's performance using simulated ECG signals and demonstrate its efficacy in detecting various types of cardiac arrhythmias. The proposed smart biomedical sensor network has the potential to revolutionize cardiac arrhythmia detection by providing real-time monitoring, improving patient outcomes, and reducing healthcare costs. The system can alert healthcare professionals to potential cardiac arrhythmias before they become life-threatening, allowing for early intervention and prevention. The use of wearable sensors can also improve patient comfort and mobility, allowing for continuous monitoring in a non-invasive manner. Overall, the proposed system offers a cost-effective and efficient solution for multi-patient cardiac arrhythmia detection, which has the potential to improve patient outcomes and reduce healthcare costs significantly. Further research and development are needed to optimize the system's design and performance, but the proposed smart biomedical sensor network is an exciting development in the field of cardiac arrhythmia detection.