Precise risk assessments are essential to properly intervene and prevent cardiovascular diseases (CVDs), among the main causes of death worldwide. Though useful, conventional risk factors, including smoking status, cholesterol levels, and blood pressure, do not necessarily provide the complete picture of cardiovascular risk. Results of this research confirm the need to use several strategies to evaluate CVD risk. It does this by combining real-time wearable technology data with traditional risk variables gathered from Internet health sources. Wearable devices like smartwatches and fitness trackers provide real-time insights about a person’s health state by continuously tracking measures including heart rate, oxygen saturation, and body temperature. These gadgets enhance the accuracy of risk prediction models when utilized alongside traditional risk indicators. The researchers developed prediction models utilizing a combination of machine learning techniques, including random forest, Support Vector Machine (SVM), and gradient boosting, with these integrated data sources. Performance of the model is evaluated against accuracy, area under the curve (AUC), precision, recall, and F1-score. Random forest was the most successful model, having a 99% general accuracy and an AUC of 1.00. The findings underline the vital relevance of blood pressure as a predictor of CVD risk even if other variables, such as age and heart rate, show weaker relationships. Early risk factor identification and more tailored treatment plans made feasible by this method serve to enhance cardiovascular care. Guaranteeing the generalizability and durability of the model depends on more validation on several datasets.