International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com INDIRAPRIYADARSHINI 7 4 (July - August) 2025 07 July-August enStroke PredictionEnsemble LearningXG- BoostMLPBidirectional LSTM-GRU. Stroke remains one of the foremost causes of mortality and longterm disability worldwide Early prediction of stroke is crucial in enabling timely medical interventions and minimizing its adverse outcomes This study presents a robust ensemble based machine learning framework that integrates the predictive strengths of XGBoost a MultiLayer Perceptron MLP and a hybrid Bidirectional LSTMGRU neural network to enhance the accuracy of stroke prediction The model is trained and validated using a publicly available healthcare dataset containing a diverse set of clinical and demographic features such as age hypertension heart disease smoking status and body mass index BMI Comprehensive data preprocessing steps including missing value imputation feature encoding and class balancing using Random Over Sampler were implemented to prepare the dataset for training Each individual model was optimized for performance and then combined in an ensemble using weighted averaging to maximize generalization and robustness The pro posed ensemble model demonstrated superior performance with an accuracy of 9458 precision of 9064 recall of 9945 F1 score of 9484 and an impressive ROCAUC score of 9873 The high recall indicates that the model is particularly effective in identifying strokepositive cases which is critical in a clinical setting The confusion matrix and classification report further support the models reliability and effectiveness Additionally visualization techniques such as ROC curves precisionrecall curves and calibration plots confirmed the models strong predictive confidence Feature importance analysis highlighted the significant impact of variables such as age hypertension and BMI This ensemble framework presents a powerful tool for clinical decision support with potential deployment in real time health monitoring systems for early stroke detection and intervention Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2025 All Rights Reserved