Monitoring cardiovascular conditions is crucial in healthcare due to their significant impact on overall wellness and their role in mitigating heart-related diseases. To address this pressing issue, the research community has introduced various methodologies, among which deep learning approaches have shown notable effectiveness. Despite this potential, creating effective deep learning models tailored to time-series health data remains challenging. These challenges include processing vast amounts of data from IoT devices, building and selecting optimal deep learning models with appropriate parameters, and designing and implementing reliable systems for cardiovascular health monitoring. In response, our research introduces an advanced cardiovascular health monitoring system that takes advantage of wearable IoT and deep learning technologies to enhance healthcare. It features a multi-layered architecture, where each layer serves a specific function and integrates closely with the others. This integration enhances the system’s overall functionality and reliability. The system efficiently integrates processes from health data collection through deep learning analysis to the delivery of timely health alerts. A critical feature of this system is the targeted deep learning model, selected from six potential algorithms based on experiments with data from IoT-enabled smartwatches. The selection process involves an in-depth evaluation of the models’ performance, leading to the choice of the most effective model for system implementation. Our results highlight the system’s effectiveness in monitoring cardiovascular health, underscoring its potential to enhance personalized healthcare, particularly for individuals with cardiovascular conditions, through advanced monitoring technologies.