Globally, cardiovascular diseases (CVDs) remain the most common cause of death, posing a significant public health issue. This initiative aims to address this issue by using a unique system that combines wearable technology with a web-based application for continuous cardiovascular risk monitoring and prediction. The first component of the system architecture is a wearable device with sensors capturing vital physiological data, including heart rate, systolic blood pressure (BP), diastolic BP, oxygen saturation (SpO₂), body temperature, and breathing rate. These parameters define possible risk and help one monitor cardiovascular diseases. The developed device underwent testing. The device measured heart rates ranging from 64 to 151 beats per minute, systolic blood pressure from 87 to 154 mmHg, and diastolic blood pressure from 64 to 119 mmHg. Although respiratory rate fluctuated from 16 to 25 breaths per minute and body temperature ranged from 34.5 °C to 37.3 °C, oxygen saturation (SpO₂) levels fell between 11.8% and 85.1%. The captured data was transferred in real time via Wi-Fi to a web-based application for further study. The second component of the system is a predictive web application, which comprises machine learning algorithms trained and tested to analyze physiological data. The program provides personalized health insights and projections, along with a real-time evaluation of cardiovascular risks. Testing and calibration helped us to guarantee the correctness and dependability of the wearable gadget as well as the Internet application. Using real-time data and predictive modeling, this system detects cardiovascular hazards early, giving users and medical specialists valuable information for quick interventions. Ultimately, this approach might assist in lowering the global CVD load and improving cardiovascular health results.