Cardiovascular diseases (CVDs) remain one of the leading causes of mortality and disability worldwide, accountingforasignificant proportion of premature deaths and healthcare expenditures. Early identification of individuals at highriskofcardiovascular disease enables timely clinical intervention, personalized treatment planning, and improved patient outcomes. Theincreasing availability of electronic health records (EHRs), clinical laboratory reports, physiological measurements, andpatientdemographic information has created new opportunities for applying machine learning techniques to cardiovasculardiseaseprediction. During the period between 2008 and 2015, substantial research was devoted to medical data mining, clinical decisionsupport systems, risk prediction models, and intelligent healthcare analytics, establishing the theoretical foundationforcontemporary machine learning-based cardiovascular diagnosis. This study proposes a Machine Learning FrameworkforEarlyPrediction of Cardiovascular Diseases Using Clinical Data (MLF-CVD) that integrates clinical data acquisition, preprocessing,feature selection, machine learning classification, cardiovascular risk assessment, and clinical decision support intoaunifiedpredictive framework. The proposed framework investigates widely adopted machine learning algorithms including DecisionTrees,Support Vector Machines (SVM), Artificial Neural Networks (ANN), Naïve Bayes, Logistic Regression, RandomForests, andensemble learning techniques for early cardiovascular disease prediction. Keywords: Cardiovascular Disease, Machine Learning, Clinical Data, Early Disease Prediction, Clinical Decision Support