This dissertation addresses critical challenges in cardiovascular health monitoring, focusing on the potential of cardiomechanical signals to improve early detection and management of cardiovascular diseases (CVDs). Given the worldwide prevalence of CVDs, the research underscores the importance of precision health and the role of wearable technology in revolutionizing patient monitoring. Through a series of scientific contributions, this work tackles key issues such as data scarcity, noise interference, and the need for sophisticated signal analysis techniques. First, we introduce deep generative models to augment cardiomechanical signal datasets, allowing for more extensive and diverse data for research and application development. We also describe the design of hardware phantoms to safely and reliably replicate human cardiomechanical signals for testing and validation purposes. Next, we introduce SeismoNet, a multi-node wearable platform that collects signals from multiple body points, improving signal quality and reducing noise and interference, enhancing the accuracy of health parameter estimations. Then, we design advanced deep learning models for denoising cardiomechanical signals, specifically addressing both stationary and non-stationary noise through architectures including U-Net and residual U-Net. Finally, we investigate a two-step approach of pre-training and task-specific optimization to refine the models for specific health monitoring tasks, demonstrating effectiveness in monitoring shunts in ductal dependent infants. By bridging the gaps associated with the utilization of cardiomechanical signals, the research within this dissertation paves the way for healthcare solutions that are more personalized, accurate, and accessible, aligned with the overarching goal of diminishing the global burden of cardiovascular diseases.