Objective To develop a prediction model for hemorrhagic transformation after endovascular treatment of acute cerebral infarction (ACI) based on LASSO and random forest algorithms, and to verify its performance.Methods Patients with ACI who underwent endovascular treatment at Heyuan People's Hospital between January 2021 and December 2024 were enrolled. Using simple random sampling, the patients were randomly assigned in a 7:3 ratio to a training group (n = 280) and a validation group (n = 120). Clinical data of all patients were collected. LASSO regression was used to identify key variables, which were subsequently entered into multivariable logistic regression analysis to identify independent risk factors. A prediction model for postprocedural hemorrhagic transformation after endovascular treatment for ACI was then developed using a random forest algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical utility of the model, respectively.Results The incidence of postprocedural hemorrhagic transformation among the 400 patients with ACI was 17.00% (68/400). Based on the λ1se criterion, LASSO regression selected 12 key variables from 32 candidate variables. Multivariable logistic regression analysis showed that atrial fibrillation [O^R = 4.308 (95% CI: 1.688, 10.998) ], higher preoperative systolic blood pressure (SBP) [O^R = 1.036 (95% CI: 1.012, 1.062) ], lower platelet (PLT) count [O^R = 0.979 (95% CI: 0.968, 0.991) ], higher C-reactive protein (CRP) level [O^R = 1.140 (95% CI: 1.034, 1.257) ], bridging therapy [O^R = 3.060 (95% CI: 1.262, 7.423) ], prolonged puncture to recanalization (PTR) time [O^R = 1.059 (95% CI: 1.021, 1.098) ], a greater number of thrombectomy device passes [O^R = 3.030 (95% CI: 1.607, 5.711) ], the use of two antithrombotic agents [O^R = 3.985 (95% CI: 1.551, 10.236) ], and the use of loading doses of antithrombotic agents [O^R = 5.149 (95% CI: 1.845, 14.375) ] were independent risk factors for postprocedural hemorrhagic transformation after endovascular treatment for ACI (all P < 0.05). ROC curve analysis showed that the area under the curve (AUC) values were 0.884 (95% CI: 0.832, 0.935) and 0.876 (95% CI: 0.813, 0.938) in the training and validation groups, respectively. The Hosmer-Lemeshow test showed no significant differences between the predicted and observed outcomes in either group (both P > 0.05). DCA showed that the random forest model provided a higher standardized net benefit across threshold probabilities ranging from 0.05 to 1.00. SHapley Additive exPlanations (SHAP) analysis showed that the dose of antithrombotic medication contributed most to the model predictions, and lower feature values were associated with an increased risk of hemorrhagic transformation. Individual prediction analysis showed that the patient's baseline risk was 0.0496 and decreased to 0.0140 after the cumulative contribution of the individual predictors.Conclusion The prediction model developed in this study accurately predicts the occurrence of postprocedural hemorrhagic transformation after endovascular treatment in patients with ACI and may provide a reference for the formulation of postoperative treatment strategies.