The world’s top cause of death is cardiovascular diseases (CVDs), hence improved early detection and intervention strategies are needed. Predictive analytics, which makes use of machine learning (ML) techniques and the vast data repositories made available by Electronic Health Records (EHRs), has enormous potential for precisely assessing the risk of CVD. This study looks at feature selection, model training, data preparation, and evaluation techniques when using predictive analytics to assess CVD risk using EHR data. The top models for this challenge are determined by comparing a number of machine learning techniques, such as Random Forest, Support Vector Machines (SVM), Logistic Regression, Neural Networks, and Gradient Boosting Machines (GBM). Data privacy, class imbalance, model interpretability, and bias mitigation are further issues that are addressed in the paper, along with solutions. ML-driven prediction models can enhance clinical judgment, maximize the use of healthcare resources, and improve patient outcomes, as the results show. While upholding ethical standards and guaranteeing model openness, the study emphasizes how crucial it is to incorporate these models into clinical procedures.