Alzheimer's disease (AD) necessitates early diagnosis and monitoring for effective management. This study introduces AlzNet, an AI-powered algorithm that integrates the strengths of deep neural networks (DNNs) and support vector machines (SVMs) to analyze cerebrospinal fluid (CSF) biomarkers—amyloid-beta (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau181). Leveraging data from 500 participants (200 AD, 150 mild cognitive impairment (MCI), 150 healthy controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), AlzNet demonstrated high accuracy (93.2%), sensitivity (88.7%), specificity (95.4%), and AUC-ROC (0.94) in differentiating between AD, MCI, and controls. Notably, it identified lower Aβ42 and elevated t-tau and p-tau181 levels as significant markers.AlzNet's non-invasive, cost-effective approach and its potential to facilitate early detection and continuous monitoring of AD underscore its clinical utility. Future research will explore its validation across diverse populations and enhance real-time monitoring capabilities.