In an era of healthcare revolution, an accurate prediction of Alzheimer’s disease plays key role but it is challenging due to its complexity and diversity in contributing attributes. This paper investigates some advanced prediction models while focusing on ensemble strategies and feature selection. With analysis of biomarkers, clinical data, and neuroimaging metrics, this paper aims to enhance model accuracy and hence better results. Feature selection methods like including filter and embedded techniques are analyzed to identify key predictors. Ensemble techniques like bagging and stacking are explored to combine model predictions for better performance and robustness. This paper addresses effective Alzheimer’s disease prediction models and thus contributing proven and valuable insights and methodologies for future research and clinical applications.