The interaction effect between collateral circulation and ischemic core size on stroke outcomes has been highlighted in acute ischemic stroke (AIS). However, biomarkers that assess the magnitude of this interaction are still lacking. We aimed to present a new imaging marker, the collateral-core ratio (CCR), to quantify the interaction effect between these factors and evaluate its ability to predict functional outcomes using machine learning (ML) in AIS. Patients with AIS caused by anterior circulation large vessel occlusion (LVO) were recruited from a prospective multicenter study. CCR was calculated as collateral perfusion volume/ischemic core volume. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. An ML model was built and tested with a tenfold cross-validation using nine clinical and four imaging variables with mRS score 3-6 as unfavorable outcomes. Among 129 patients, CCR was identified as the most important variable. The prediction model incorporating clinical factors, ischemic core volume, collateral perfusion volume, and CCR showed better discriminatory power in predicting unfavorable outcomes than the model without CCR (mean C index 0.853 +/- 0.108 versus 0.793 +/- 0.133, P = 0.70; mean net reclassification index 52.7% +/- 32.7%, P < 0.05). When patients were divided into two groups based on their CCR value with a threshold of 0.73, unfavorable outcomes were significantly more prevalent in patients with CCR <= 0.73 than in those with CCR > 0.73. CCR is a robust predictor of functional outcomes, as identified by ML, in patients with acute LVO. The prediction model that incorporated CCR improved the model's ability to identify unfavorable outcomes. ClinicalTrials.gov Identifier: NCT02580097.