GENETIC BIOMARKERS DETECTION FOR ALZHEIMER’S DISEASE**

Alzheimer’s disease remains a complex condition with an unclear cause and no known cure.Current treatments focus on symptom management and slowing disease progression. Research is ongoingto uncover its underlying mechanisms, develop effective treatments, and explore early detection andprevention strategies.Genetic data plays a crucial role in Alzheimer’s detection, offering significant advantages. Genome-wideassociation studies (GWAS) have identified numerous genetic variants linked to the disease. Large-scalegenetic analyses help researchers understand disease pathways, identify potential drug targets, andcontribute to novel therapeutic developments.This review aims to highlight research gaps and limitations while proposing future directions foradvancing the field. It provides a detailed survey outlining essential criteria for improving genetic-baseddetection methods. Researchers can enhance accuracy by selecting optimal approaches for geneticanalysis. The review focuses on recent studies that integrate genetic data with artificial intelligence (AI) toidentify mutated genes associated with Alzheimer’s and classify the disease efficiently.Findings indicate that, despite a relatively small body of published research, studies in this field have grownexponentially since 2020. This review offers a comprehensive analysis of genetic and AI-driven approachesfor Alzheimer’s detection. It serves as a valuable resource for researchers, clinicians, and policymakers,shedding light on the current state of the field, guiding future research, and supporting the development ofmore accurate and effective early detection methods for Alzheimer’s disease.