Neurodegenerative illnesses, especially Alzheimer’s disease (AD), pose considerable issues in healthcare owing to their progressive characteristics and the subtlety of initial symptoms. Identifying these disorders is essential for executing therapies that can decelerate development and enhance patient outcomes. Traditional diagnostic methods, which predominantly depend on clinical assessments and neuroimaging techniques, are frequently laborious and may lack precision, particularly in the prodromal phases. This research examines the utilization of AI-driven predictive models for the early identification of neurodegenerative disorders, specifically Alzheimer’s disease. We assess the efficacy of various machine learning models, including Logistic Regression, Support Vector Machines (SVMs), Decision Trees, Random Forest, and AdaBoost, utilizing the publicly accessible ‘Detecting Early Alzheimer’s’ dataset, which comprises MRI data and cognitive test results. These models are evaluated using essential performance indicators, including accuracy, precision, recall, F1-score, and ROC-AUC. Random Forest and AdaBoost are the leading models, with Random Forest attaining the best testing accuracy and F1-score and enhanced discriminative ability demonstrated by its ROC-AUC score. A thorough comparative study indicates that the suggested models surpass current methods in early diagnosis accuracy. Furthermore, visual representations, including bar charts, clearly compare precision, recall, F1-score, and testing accuracy. The findings indicate that ensemble learning techniques such as Random Forest and AdaBoost are notably proficient in identifying early-stage Alzheimer’s, establishing a dependable basis for incorporating AI into clinical diagnostic processes for neurodegenerative disorders.