Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, accounting for approximately 17.9 million deaths annually. The increasing burden of CVDs highlights the critical need for timely diagnosis, continuous monitoring, and early intervention. Traditional clinical approaches to cardiovascular monitoring are often episodic, resource-intensive, and limited to healthcare facilities, thereby constraining proactive disease management. In response, wearable health technologies have emerged as a transformative solution by enabling real-time, non-invasive, and continuous tracking of vital cardiovascular parameters such as electrocardiogram (ECG), photoplethysmography (PPG), heart rate variability (HRV), and blood pressure. These devices, ranging from smartwatches to ECG patches, generate vast amounts of physiological data, which, when integrated with advanced computational tools, can significantly enhance cardiovascular care. Deep learning, a subfield of artificial intelligence, plays a pivotal role in extracting meaningful insights from this high-dimensional and time-series data. Techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models have demonstrated exceptional capabilities in arrhythmia detection, blood pressure estimation, and early prediction of cardiac anomalies. This review synthesizes the latest advancements in wearable-based cardiovascular monitoring, with a focus on the integration of deep learning algorithms for real-time data analysis. We explore the types of wearable sensors, signal processing methods, deep learning architectures, and system-level implementations. Furthermore, we discuss the clinical implications, current limitations, regulatory landscape, and future directions, including edge AI, federated learning, and multi-modal data integration. The convergence of wearable technologies and deep learning holds the potential to revolutionize cardiovascular healthcare by transitioning from reactive to predictive and personalized medicine.