BACKGROUND: Novel imaging and analysis techniques may offer the ability to detect noncalcified or high-risk coronary plaques on a non-contrast computer tomography (CT) scan, advancing cardiovascular diagnostics. AIMS: We aimed to explore whether machine learning (ML) radiomic analysis of low-dose high-resolution non-contrast electrocardiographically (ECG) gated cardiac CT scan allows for the identification of noncalcified coronary plaque characteristics. METHODS: We prospectively enrolled 125 patients with noncalcified plaques and adverse plaque characteristics (APC) and 25 controls without visible atherosclerosis on coronary CT angiography (CCTA). All patients underwent non-contrast CT exam before CCTA. Four hundred and nineteen radiomic features were calculated to identify the presence of any coronary artery disease (CAD), obstructive CAD (stenosis >50%), plaque with ≥2 APC, degree of calcification, and specific APCs. ML models were trained on a training set (917 segmentations) and tested (validation) on a separate set (292 segmentations). RESULTS: Among the radiomic features, 88.3% were associated with a plaque, 0.9% with obstructive CAD, and 76.4% with the presence of at least two APCs. Overall, 80.2%, 88.5%, and 36.5%, of features were associated with calcified, partially calcified, and noncalcified plaques, respectively. Regarding APCs, 61.1%, 61.8%, 84.2%, and 61.3% of features were associated with low attenuation (LAP), napkin-ring sign (NRS), spotty calcification (SC), and positive remodeling (PR), respectively. ML models outperformed conventional methods for the presence of plaque obstructive stenosis, and the presence of 2 APCs, as well as for noncalcified plaques and partially calcified plaques, but not for calcified plaques. ML models also significantly outperformed identification of LAP and PR, but neither NRS nor SC. CONCLUSION: Radiomic analysis of non-contrast cardiac CT exams may allow for the identification of specific noncalcified coronary plaque characteristics displaying the potential for future clinical applications.