The early detection of Alzheimer’s Disease (AD) is crucial for effective intervention; nevertheless, existing diagnostic algorithms frequently encounter challenges related to data heterogeneity and inadequate feature engineering. This work offers a systematic methodology for Alzheimer’s disease identification using comprehensive exploratory data analysis, refined preprocessing, and the creation of interaction-based characteristics. We assess various machine learning models and determine that the Support Vector Machine (SVM) utilising a Radial Basis Function (RBF) kernel is the most successful, with an accuracy of 89%, surpassing other advanced techniques. Our findings underscore the essential importance of meticulous data transformation and model optimisation in improving diagnostic accuracy, thus providing a scalable and interpretable approach for early Alzheimer’s disease prediction in clinical environments.