Developed an ensemble learning model to predict the onset of Alzheimer’s disease, achieving a high accuracy of 95.9%. Conducted a comparative analysis of individual machine learning classifiers including Logistic Regression, SVM, and Decision Trees against the ensemble approach to improve diagnostic reliability in medical applications. The project culminated in a research paper titled “Comprehensive Methodologies for Alzheimer’s Disease Prediction: A Machine Learning Approach,” which was published in the proceedings of the 2nd IEEE International Conference on IoT, Communication and Automation Technology, held at Buddha Institute of Technology, Gorakhpur, in November 2024.