Identifying stroke patients at risk of postthrombolysis intracranial hemorrhage (ICH) in the clinical setting is essential. We aimed to develop and evaluate a nomogram for predicting the probability of ICH in acute ischemic stroke patients undergoing thrombolysis. Patients and Methods: A retrospective observational study was conducted using data from 345 patients at a single center. The patients were randomly dichotomized into training (2/3; n=233) and validation (1/3; n=112) sets. A prediction model was developed by using a multivariable logistic regression analysis. Results: The nomogram comprised three variables: the presence of atrial fibrillation (odds ratio [OR]: 4.92, 95% confidence interval [CI]: 2.09-11.57), the National Institutes of Health Stroke Scale (NIHSS) score (OR: 1.11, 95% CI: 1.04-1.18) and the glucose level on admission (OR: 1.27, 95% CI: 1.08-1.50). The areas under the receiver operating characteristic curve of the nomogram for the training and validation sets were 0.828 (0.753-0.903) and 0.801 (0.690-0.911), respectively. The Hosmer-Lemeshow test revealed good calibration in both the training and validation sets (P = 0.509 and P = 0.342, respectively). The calibration plot also demonstrated good agreement. A decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: We developed an easy-to-use nomogram model to predict ICH, and the nomogram may provide risk assessments for subsequent treatment in stroke patients undergoing thrombolysis.