In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by advanced methodologies such as machine learning (ML) and deep learning (DL). The AI-driven techniques leverage large datasets to develop and train algorithms capable of making precise evaluation and predictions. The realm of cardiac CT is expanding day by day and multiple tools are offered to answer different questions. Coronary artery calcium score (CACS) and CT angiography (CTA) provide high-resolution images that facilitates detailed anatomical evaluation of coronary plaque burden. New tools such myocardial CT perfusion (CTP) and fractional flow reserve (FFR _CT) have been developed to add a functional evaluation of the stenosis. Seen the great added value of the aforementioned tools, the demand for new exams has increased such as the burden on imagers. Due to its ability to fast compute multiple data, AI can be helpful in both the acquisition and post-processing phases. AI can possibly reduce radiation dose, increase image quality and shorten image analysis time. Moreover, different types of data can be used for risk assessment and patient risk stratification. Recently, the focus of the scientific community on AI has led to numerous studies, especially on CACS and CTA. This narrative review concentrates on AI’s role in the post-processing of CACS, CTA, FFR _CT and CTP, discussing both current capabilities and future directions in the field of cardiac imaging.