Automated analysis of electrocardiogram (ECG) data has emerged as a valued tool in clinical work, offering the potential to enhance diagnostic accuracy, reduce workload for healthcare professionals, and enable timely detection of cardiac abnormalities. We have developed an advanced deep-learning-based system capable of automatically detecting and classifying a wide range of ECG signal patterns with high precision. With the increased availability of physiological data from wearable sensors, hospital monitoring systems, and electronic health records, data-driven methods for cardiovascular signal interpretation have grown in popularity. Because deep learning can uncover complex patterns from large volumes of high-dimensional biological data, it has emerged as a powerful tool among them. This study provides a comprehensive analysis of deep learning techniques used in cardiovascular signal processing, focusing on modalities such as electrocardiograms (ECG), phonocardiograms (PCG), and photoplethysmograms (PPG). We evaluate the performance of various architectures, including Transformer-based models for attention-based analysis and sequence-to-sequence learning, for spatial feature and temporal dependencies. Hybrid architectures and encoder-decoder frameworks are investigated for more complicated tasks, including beat segmentation, arrhythmia classification, and cardiac event prediction. The integration of multi-modal data sources and the function of federated learning in protecting patient privacy during the training of large-scale models are also examined. When these models are implemented with Internet of Things (IoT) devices and edge computing systems, the potential for individualized diagnosis, real-time monitoring, and early intervention is significantly increased.