A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. The proposed model is an ensemble machine learning algorithm, which integrates predictions obtained from several individual classifiers like Random Forest, Decision tree, KNN (K-nearest neighbor), voting Classifier, Logistic regression, XG Boost (Extreme Gradient Boosting) to make a final prediction. Each classifier provides a probability estimate for each class, final prediction is based on weighted average of these probabilities. The weights assigned to each classifier can be based on their performance on a validation set or can be set uniformly. The proposed voting classifier improved accuracy and robustness of final prediction compared to a single classifier. The limitation of study classification of stroke type can lead to appropriate use of resources and help to reduce healthcare costs. The proposed model obtained an accuracy of 100%. In order to carry out the investigation, the stroke prediction dataset is collected from UCI machine learning repository. The proposed system analyzes medical datasets containing patient attributes such as age, blood pressure, glucose level, heart disease history, and imaging data. Feature preprocessing and data balancing techniques are applied to improve model performance. The ensemble models are evaluated using performance metrics including accuracy, precision, recall, and F1-score.