Cardiac magnetic resonance imaging (CMRI) enables the diagnosis of coronary artery disease (CAD) by analyzing cardiac images to detect blockages in the coronary arteries. This helps make a good diagnosis and clinical decision-making regarding further treatment. An intelligent clinical support system for CAD diagnosis was developed in this study, combining explainable artificial intelligence (XAI) with deep learning (DL). The proposed model used CMRI for diagnosis, while modified gradient-weighted class activation mapping was used for cardiac image segmentation. The segmented image, required to further simplify prediction, was then used in SHapley Additive exPlanations (SHAP) for feature selection (FS). The transformer-aided deep neural network (T-DNN) received this FS output for classification. The T-DNN learned contextual relations, thereby increasing the model’s overall efficiency. The proposed model’s segmentation mechanism was tested using the Dice similarity coefficient (DSC) and pixel accuracy. The DSC of the model was 0.93, indicating a strong overlap between the predicted and ground-truth segmented regions. The achieved classification accuracy of 88% surpasses that of state-of-the-art techniques. These results demonstrate that combining XAI with DL yields accurate, explainable CAD diagnosis and treatment, thereby automating and simplifying clinical trials.