Alzheimer's Disease (AD) constitutes a progressive neurodegenerative pathology that undermines cognitive faculties, mnemonic capacity, and the aptitude for executing quotidian tasks in geriatric individuals, frequently accompanied by alterations in demeanor and temperament. In the absence of a definitive cure, extant therapeutic interventions demonstrate optimal efficacy during the initial and intermediary phases of AD, underscoring the criticality of precocious diagnosis amidst the burgeoning geriatric demographic and the concomitant escalation in AD prevalence. This inquiry endeavors to discern incipient indicators of AD by harnessing machine learning and deep learning methodologies to scrutinize MRI imagery derived from the Open Access Series of Imaging Studies (OASIS) dataset. A spectrum of algorithms was deployed, encompassing Random Forest and Logistic Regression within traditional machine learning paradigms, Extra Trees as an exemplar of ensemble learning, and Convolutional Neural Networks (CNN) representative of deep learning architectures. The efficacy of these models was appraised through metrics including accuracy, precision, recall, and area under the curve (AUC), wherein CNN exhibited preeminent accuracy and AUC metrics, while Extra Trees distinguished itself in precision and recall, substantiating the capacity of both deep learning and ensemble learning modalities to deliver robust performance in the early detection of AD.