As an innovative imaging processing mode, radiomics can extract microscopic information from images for quantitative analysis. The selected features and machine learning model can provide valuable data for clinical decisions in heart disease. Up till now, several studies have demonstrated the role of radiomics in the accurate diagnosis and discrimination of heart disease as well as in the prognosis assessment of the patient with heart disease. Cardiac Magnetic Resonance (CMR) displays a wide range of advantages, such as multi-parameter, multi-sequence, multi-plane, and no radiation. CMR has advantages in noninvasive assessment of structural and functional heart disease. This paper reviews the workflow and related studies on common heart disease based on CMR images in radiomics.