Machine learning in Alzheimer’s disease (AD) detection is an active research area that leverages computational models to improve early diagnosis, progression prediction, and personalized intervention planning. Traditional diagnostic approaches, including clinical assessments, cognitive tests, and neuroimaging, often detect the disease at advanced stages and are limited by subjectivity. Machine learning techniques analyze diverse data sources such as MRI and PET scans, genetic markers, cerebrospinal fluid (CSF) biomarkers, speech patterns, and cognitive test scores to identify early indicators and subtle patterns associated with AD. Early studies employed classical algorithms like support vector machines (SVM), decision trees, k-nearest neighbors (KNN), and random forests for classification and prediction tasks. Recent research integrates deep learning architectures, including convolutional neural networks (CNNs) for imaging analysis, recurrent neural networks (RNNs) and LSTMs for temporal cognitive and behavioral data, and hybrid models for multi-modal data fusion. Applications include early diagnosis, prediction of disease progression, patient stratification, and support for treatment planning. Current studies also explore explainable AI for clinical interpretability, transfer learning for data-efficient modeling, and federated learning for privacy-preserving collaboration, establishing machine learning as a key enabler for accurate, scalable, and personalized Alzheimer’s disease detection and management.