Directed by 2 decades of technological processes and remodeling, the dynamic quality of healthcare data combined with the progress of computational power has allowed for rapid progress in artificial intelligence (AI). In interventional cardiology, artificial intelligence has shown potential in providing data interpretation and automated analysis from electrocardiogram, echocardiography, computed tomography angiography, magnetic resonance imaging, and electronic patient data. Clinical decision support has the potential to assist in improving patient safety and making prognostic and diagnostic conjectures in interventional cardiology procedures. Robot-assisted percutaneous coronary intervention, along with functional and quantitative assessment of coronary artery ischemia and plaque burden on intravascular ultrasound (IVUS), are the major applications of AI. Machine learning algorithms are used in these applications, and they have the potential to bring a paradigm shift in intervention. Recently, an efficient branch of machine learning has emerged as a deep learning algorithm for numerous cardiovascular applications. However, the impact deep learning on the future of cardiology practice is not clear. Predictive models based on deep learning have several limitations including low generalizability and decision processing in cardiac anatomy.