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Coronary heart disease (CHD) remains one of the leading health issues worldwide, primarily caused by plaque development that leads to narrowing of coronary arteries. Early identification of such conditions is crucial. Coronary computed tomography angiography (CCTA) is a common diagnostic tool for assessing coronary artery conditions. CCTA requires manual interpretation by a radiologist. We developed a web-based interface in which doctors or patients can upload their CCTA images and get the results. This paper presents an AI- based system that enhances CCTA image analysis through a Recurrent Convolutional Neural Network (RCNN). The proposed system automatically identifies plaque presence within coronary arteries and determines the degree of stenosis. The system achieves 91% accuracy.