Objective To explore the prognostic implications of coagulation-fibrinolysis biomarkers in intracerebral hemorrhage （ICH） and to construct a multivariable logistic regression model for individualized risk prediction. Methods A total of 101 ICH patients who were admitted to Nanfang Hospital of Southern Medical University from January 2020 to December 2023 were retrospectively enrolled. These patients were stratified into a poor outcome group （ΔGCS ≤ 0） and a good outcome group （ΔGCS > 0） according to the difference in Glasgow Coma Scale （GCS） scores between discharge and admission. Coagulation and fibrinolysis markers collected upon admission were analyzed. The Least Absolute Shrinkage and Selection Operator （LASSO） regression was employed to screen variables. A logistic regression model was constructed using 70% of the cases （the training set）， while the remaining 30% were utilized for validation. The performance of the model was evaluated through receiver operating characteristic （ROC） curves， calibration plots， Hosmer-Lemeshow goodness-of-fit test， and decision curve analysis （DCA）. Results Univariate analysis indicated that thrombin-antithrombin complex （TAT）， D-dimer， and age exhibited significant differences between the two outcome groups （P < 0.05）. These three variables were selected via LASSO regression and incorporated into the logistic model. The final model equation was expressed as： logit（P） = -6.234 + 1.132 × TAT + 0.867 × D-dimer + 0.699 × Age. In the training set， the area under the ROC curve （AUC） was 0.795. The calibration curve demonstrated excellent agreement between the predicted and observed outcomes， with a Hosmer-Lemeshow test P-value of 0.8568. DCA revealed that the model achieved net clinical benefit across a broad range of risk thresholds （0.1 ~ 0.8）. Conclusions TAT， D-dimer， and age are independent predictors of poor prognosis in patients with ICH. The logistic regression model based on these variables demonstrates favorable discriminatory ability and clinical utility. The nomogram derived from this model enables individualized risk assessment and may aid clinicians in early prognostic evaluation and treatment planning.