The study has proposed an optimized intelligent attribute reduction, risk prediction, and classification framework using Machine Learning for early identification of Atherosclerotic Cardiovascular (ASCV) risk status. Data preprocessing with normalization and missing data imputation using Multiple Imputation (MI) with Gaussian Copula (GC) method was done before optimal attribute selection. The proposed framework represented as SVMGWO-ANFISGWO-Multi-SVM has used Grey Wolf Optimization (GWO) algorithm. Feature selection was based on fitness evaluated wrapper-based multi-Support Vector Machine (multi-SVM). The optimal features selected by SVMGWO are processed by ANFISGWO for optimal ASCV risk prediction. Finally, multi-SVM is used to identify the risk class of the predicted ASCV risk. Performance of the study model were evaluated using Accuracy, Sensitivity, Specificity, Precision, G-mean, and F-measure features. Confusion and ROC plots are used to identify the classier performances. The Area Under Curve (AUC) was plotted to study the discrimination between the ASCV risk classes. The implementation was done in MATLAB 2015b. The ANFISGWO in the training phase has shown 80.7% accuracy, 100% sensitivity, 77.4% specificity, 43.3% precision, 60.2% G-mean, and 87.9% F-measure. Whereas, in the testing phase, the accuracy was 80%, 100% sensitivity, 77.1% specificity, 38.1% precision, 54.4% G-mean, and 87.7% F-measure. The overall AUC of 0.976 shows good discrimination between the ASCV risk classes.