Futile recanalization is a recognized challenge in acute ischemic stroke (AIS) patients after endovascular treatment (EVT). Our purpose was to develop and validate a predictive model for futile recanalization after EVT by integrating arterial-venous collateral assessment with clinical parameters. This study included 392 AIS patients with acute anterior circulation large vessel occlusion who underwent EVT (March 2016-June 2024). Patients were stratified into training (n = 160), internal validation (n = 69), and completely independent external validation (n = 163) cohorts collected from a separate medical center. Predictors were identified using Boruta algorithm and LASSO regression. Multiple machine learning models were evaluated through discrimination, calibration, and decision curve analyses, with SHAP analysis for feature importance. Three independent predictors were identified: age (OR: 1.06, 95% CI: 1.02-1.11), whole-brain arterial collateral status (OR: 0.30, 95% CI: 0.18-0.50), and whole-brain venous collateral status (OR: 0.78, 95% CI: 0.67-0.90). The model demonstrated excellent discrimination in the training cohort (AUC: 0.914, 95% CI: 0.866-0.963), internal validation cohort (AUC: 0.918, 95% CI: 0.844-0.991), and notably maintained robust performance in the completely independent external validation cohort (AUC: 0.755, 95% CI: 0.678-0.832). Calibration plots showed good agreement between predicted and observed outcomes. SHAP analysis further confirmed the importance of arterial and venous collateral status assessments. The integration of whole-brain arterial-venous collateral assessment with clinical parameters shows potential value in predicting futile recanalization after EVT. This model, validated across multiple cohorts, may provide additional information to support clinical decision-making.