Alzheimer's disease (AD) represents a critical global health challenge, with early detection remaining pivotal for effective intervention and improved patient outcomes. Traditional diagnostic methods often identify the disease only after substantial neurodegeneration has occurred, limiting therapeutic efficacy. Machine learning (ML) approaches have emerged as transformative tools in revolutionizing early Alzheimer's detection through their capacity to analyze complex, multi-dimensional biomedical data. This paper provides a comprehensive examination of contemporary machine learning methodologies applied to early Alzheimer's detection, encompassing neuroimaging analysis, biomarker identification, cognitive assessment automation, and multi-modal data integration. Through systematic analysis of recent developments, this research demonstrates that ensemble learning models, particularly those integrating convolutional neural networks (CNNs) with support vector machines (SVMs), achieve diagnostic accuracies exceeding 92% in distinguishing mild cognitive impairment (MCI) from healthy controls. Deep learning architectures demonstrate superior performance in analyzing structural and functional magnetic resonance imaging (MRI) data, with classification accuracies reaching 94.7% for AD detection. However, significant challenges persist, including data heterogeneity, model interpretability, and clinical implementation barriers. This paper synthesizes current evidence, evaluates methodological approaches, and proposes future directions for advancing machine learning applications in early Alzheimer's detection within diverse clinical contexts.

Dr. Aliu Omowumi Hafsat, Hamza salim ismail,"Machine Learning Driving Approaches to Early Alzheimer's Detection" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(235-244) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.