Background: Early neurological deterioration (END) significantly worsens outcomes in acute ischemic stroke (AIS) patients receiving intravenous thrombolysis (IVT), yet clinicians lack reliable tools to identify high-risk patients who need intensified monitoring and preemptive interventions. Objective: To develop and validate a high-performance machine learning model for END prediction that enables personalized risk-stratified management of AIS patients after thrombolysis. Methods: This multicenter study analyzed 1,927 IVT-treated AIS patients from three hospitals, comprising a development cohort (n=1,361) from Lianyungang Clinical Medical College and an external validation cohort (n=566) from two independent hospitals. We systematically evaluated 27 clinical parameters using multiple machine learning algorithms to develop ENDRAS, a prediction model based on six readily available clinical variables. Model performance was assessed through comprehensive metrics (AUC, accuracy, precision, recall, F1-score) in both internal and external validation cohorts. Results: The XGBoost-based Early Neurological Deterioration Risk Assessment Score (ENDRAS) showed promising predictive performance (AUC=0.988，95% CI：0.983-0.993) using six readily available parameters: TOAST classification, intracranial artery stenosis severity, NIHSS score, systolic blood pressure, neutrophil count, and red blood cell distribution width. We established a dual-pathway management protocol stratifying patients into low-risk (<29%) and high-risk (≥29%) groups, where high-risk patients receive intensive monitoring with hourly assessments and expedited imaging, while low-risk patients follow a resource-optimized protocol without compromising safety. Implemented as a web-based calculator with <0.02-second computation time, ENDRAS enables real-time clinical decision support at the point of care. Conclusions: ENDRAS integrates END prediction into actionable clinical pathways, potentially improving post-thrombolysis care through personalized monitoring strategies and targeted interventions. Its robust performance in merged cohorts, efficient computation time, and structured management framework address key challenges in stroke care while enhancing resource utilization. Further prospective validation across diverse populations is needed to fully establish ENDRAS as a standard clinical decision-support system, but its ability to identify high-risk patients early may significantly improve outcomes in acute ischemic stroke. Clinical Trial: China Clinical Trial Registry ChiCTR2400085504;
