<p>To develop and validate an imaging-based nomogram model for assessing the risk of frequent premature ventricular contractions (PVCs) in patients with ischemic cardiomyopathy. A total of 212 patients with ischemic cardiomyopathy were randomly allocated to a training cohort and a test cohort at a ratio of 7:3. Clinical characteristics, magnetic resonance imaging (MRI) and ultrasound parameters were collected. Multivariate logistic regression analysis was performed to identify independent predictors of frequent PVCs in patients with ischemic cardiomyopathy, a nomogram model was subsequently constructed. Bootstrap resampling (<i>n</i> = 1000) was used for internal validation, the receiver operating characteristic (ROC) curve was used to evaluate discrimination performance. Calibration curves and decision curve analysis were employed to assess the model’s calibration accuracy and clinical utility. The model which was constructed based on left ventricular ejection fraction (LVEF), global longitudinal strain (GLS), total late gadolinium enhancement (LGE) extent, and Grade II-III Left ventricular diastolic function (LADD II-III) demonstrated acceptable discrimination efficiency, with an AUC of 0.86 (95% CI: 0.80–0.92) in the training cohort and 0.79 (95% CI: 0.69–0.91) in the test cohort. The C-index of the model obtained from bootstrap resampling was 0.83. The model also exhibited good calibration in both cohorts (both <i>p</i> &gt; 0.05 according to the Hosmer–Lemeshow test) and a net clinical benefit can be achieved within a reasonable probability threshold. The imaging-based model demonstrated acceptable performance in assessing the risk of frequent PVCs in patients with ischemic cardiomyopathy.</p>