Alzheimer’s disease (AD) represents a significant global health challenge due to its complex neurodegenerative nature, marked by memory loss, cognitive decline, and dementia. Early and accurate diagnosis is crucial for effective treatment and management. This study aims to develop a predictive model using the support vector machine (SVM) algorithm to identify individuals at risk of developing Alzheimer’s disease. Utilizing demographic, cognitive, and imaging data, we built an SVM model to forecast the likelihood of AD. Our findings demonstrate that the SVM model achieved a training accuracy of 96.5% and a testing accuracy of 93.9%, outperforming other machine learning models such as Decision Trees, Random Forests, Naive Bayes, Multilayer Perceptrons, and K-Nearest Neighbors. This research underscores the potential of SVM in providing a non-invasive, cost-effective, and accurate diagnostic tool for early detection of Alzheimer’s disease, thus facilitating better patient outcomes and aiding healthcare decision-making.