Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impairs cognitive and functional abilities, affecting millions globally. Early diagnosis is pivotal for slowing disease progression and improving patient outcomes. Traditional diagnostic methods, including neuroimaging and clinical evaluations, are often costly and inaccessible in many healthcare settings. This study proposes a machine learning-based predictive model for AD detection using structured tabular data encompassing demographic information, lifestyle factors, medical history, and cognitive assessments. The objective is to develop an affordable and accessible diagnostic tool that complements conventional methods. Key objectives include identifying the most predictive features, optimizing model performance through hyperparameter tuning, and ensuring interpretability for clinical applicability. Model performance is evaluated using accuracy, sensitivity, specificity. The proposed methodology and experiments offer a promising solution for early AD diagnosis with nearly 96% accuracy.