Heart disease refers to a group of disorders that affect the heart’s ability to circulate blood as well as oxygen throughout the body. Heart rhythm problems to more serious conditions like coronary artery disease or heart failure can all be the result of several illnesses, including heart disease. This paper’s main goal is to use image processing to categorize Coronary Artery Disease (CAD). Further, the major contribution is to transform Electrocardiogram(ECG) recordings into a 1-D signal using image segmentation. ECG image dataset that includes P, QRS, and T waves is used. Several classifiers that includes k-nearest neighbours(KNN), Logistic Regression (LR), Support Vector Machine(SVM), Random Forest (RF), and Decision Tree(DT) are experimented. The performance of the classifier is evaluated using widely-Accepted standard metrics, including recall, accuracy, precision, and f1-score. Finally, the performance is analyzed and presented for further research. The further research will be focused on data extraction from the image and minority eradication of the dataset.