Cardiovascular diseases (CVDs) continue to be the world's leading cause of death. It is imperative that accurate risk assessment and early intervention be implemented. This study proposes a predictive modeling framework, termed "HeartGuard," designed to assess an individual's risk of developing cardiovascular disease. Leveraging a diverse dataset comprising demographic information, lifestyle factors, medical history, and biomarker data, advanced machine learning techniques are employed to construct robust predictive models. The developed models incorporate features such as age, gender, blood pressure, cholesterol levels, smoking status, physical activity, and family history to estimate the probability of CVD occurrence within a specified timeframe. The evaluation of the models using cross-validation and independent validation datasets demonstrates their high accuracy, sensitivity, and specificity. HeartGuard offers a reliable tool for clinicians to identify individuals at heightened risk of cardiovascular disease, enabling targeted preventive measures and personalized healthcare interventions to mitigate the burden of CVD morbidity and mortality.