Introduction Artificial intelligence (AI) and machine learning (ML) models are rapidly being applied to analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Methods Six searches were performed in Medline, Embase and the Cochrane Library up to November 2021 for i) Computed Tomography-Fractional Flow Reserve (CT-FFR), ii) atrial fibrillation, iii) aortic stenosis, iv) plaque characterisation, v) fat quantification and vi) coronary artery calcium score. Results We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-fractional flow reserve (FFR) can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification (CAC) and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue (EAT) can also be automatically, accurately and rapidly quantified. Effective ML algorithms have been developed to streamline and optimise the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement (TAVR) valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of atrial fibrillation. Conclusion In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.