Background: Symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis represents a critical and fatal complication observed in acute ischemic stroke (AIS) patients. This study aims to establish a simple scoring model to predict sICH. Methods: We retrospectively conducted a cohort study of eligible AIS patients treated with rt-PA at a tertiary comprehensive stroke center from January 2018 to December 2022. Backward stepwise multivariable logistic regression provided the final model. The point score was generated from β-coefficients. The area under the curve (AUC) of the receiver operating characteristics (ROC) and the Hosmer–Lemeshow goodness-of-fit test were used to assess the discrimination and calibration of the model. The conditional probabilities were derived based on the Bayes theorem. Results: Of the included patients, sICH occurred in 26 (3.97%) of the 655. The EGAN score consisted of an early infarct sign (10 points), baseline glucose ≥200 mg/dL (11 points), atrial fibrillation (AF) (13 points), and an NIH Stroke Scale (NIHSS) score ≥10 (12 points). With a cut-off point of 13, the EGAN score demonstrated good discrimination (0.7453 [95% CI: 0.649–0.841]), sensitivity (80.77%), and specificity (58.19%), respectively, for identifying sICH. Conclusions: This easy-to-use scoring model, based on predictors quickly obtained in clinical practices, offers a simple approach to screening for post-thrombolysis sICH and can serve as an alternative option in hospitals with limited resources for thrombolysis therapy. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.