〔Abstract〕 Objective Develop an automated model that seamlessly integrates hematoma enlargement (HE) imaging data and clinical information in patients with cerebral hemorrhage (CH) and evaluate its predictive value. Methods Patients’ clinical and CT data, who underwent craniocerebral computed tomography (CT) in Shenzhen Second People's Hospital or Shenzhen Luohu District People's Hospital, were retrospectively collected between September 2016 and October 2020. The model training was divided into two parts. The first part was to develop the hemorrhagic type recognition model, which included 2836 cases of Shenzhen Second People's Hospital. The second part was to develop the HE prediction model, which included data from 530 patients with CH, including 228 patients who were positive for HE and 302 patients who were negative for HE. The external clinical validation cohort consisted of a total of 9677 patients collected from two hospitals (Shenzhen Second People's Hospital and Shenzhen Luohu District People's Hospital), which did not overlap with the first cohort. Results For the bleeding type classification model, the accuracy, sensitivity, and specificity of the 2-class model (with or without CH) all reached 100.00%. For the prediction of HE cases, the area under the receiver operation characteristic curve (AUROC) of the deep learning model based on image and clinical feature training in the internal test set and external validation cohort was 0.865 and 0.862, respectively. Conclusion The deep learning model for predicting HE can quickly and accurately predict the occurrence of HE, indicating the potential to improve diagnostic and treatment planning procedures in clinical settings.