Brain stroke is a critical medical condition that requires early diagnosis and intervention to reduce mortality and prevent severe long-term disabilities. Traditional diagnostic methods often rely on manual analysis, which can be time-consuming and prone to inaccuracies. In recent years, machine learning (ML) has emerged as a powerful tool for predictive analytics in healthcare, enabling the identification of patterns and risk factors from large datasets with high accuracy. This study focuses on leveraging advanced feature engineering and model evaluation techniques to enhance the accuracy and reliability of brain stroke prediction using machine learning algorithms.