The rising global burden of cardiovascular diseases (CVDs) has created an urgent need for efficient, continuous, and remote cardiac-monitoring systems. Recent advancements in the Internet of Things (IoT) and cloud-computing technologies enable the development of intelligent electrocardiogram (ECG) devices capable of real-time transmission, analysis, and interpretation of cardiac signals. This paper presents a comprehensive framework designed to optimize the efficiency of cloud-connected ECG-based remote cardiac diagnosis. The proposed architecture integrates low-power embedded ECG sensors, edge-level signal preprocessing, and cloud-based analytical modules to achieve reduced latency, improved data accuracy, and better energy utilization. Adaptive sampling techniques and data-compression methods are employed to minimize transmission overhead, while cloud-hosted machine-learning models are used for early detection of arrhythmia and other cardiac abnormalities. Experimental results demonstrate a 32% reduction in transmission latency and a 28% improvement in energy efficiency compared to conventional remote-monitoring approaches. These findings confirm that the proposed cloud-centric ECG architecture provides a scalable, reliable, and resource-efficient infrastructure for remote healthcare delivery, particularly in rural and resource-constrained environments.