International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com BOKKA SURI BABU 7 1 (January - February) 2025 02 January-February enData IntegrityDuplication detectionData ValidationAnomaly DetectionAutomated alerts. Stroke is still a significant global health concern that requires sophisticated predictive methods for early detection and treatment In this work a novel machine learning ML framework for automated stroke prediction is presented and its accuracy and generalization skills are evaluated against those of six wellknown classifiers In order to guarantee clear decisionmaking in clinical applications SHAP and LIME approaches are also used to highlight model interpretability By combining local and global analytical approaches the suggested framework improves the standardization of intricate machine learning models Notably Random Forest routinely achieves higher predicting accuracy than other algorithms An enhanced ensemble strategy that uses a voting mechanism to leverage numerous classifiers and incorporates CATBOOST and a Stacking Classifier is presented in order to further increase performance This study offers a thorough and trustworthy approach to early stroke diagnosis and treatment which will ultimately lessen the serious health and financial effects of this common illness Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2025 All Rights Reserved