Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with early and accurate diagnosis remaining a critical challenge. Recent advances in genomic technologies have enabled large-scale gene sequencing to identify genetic biomarkers associated with Alzheimer’s. In this project, we propose a deep learning approach leveraging Convolutional Neural Networks (CNNs) to analyze gene sequencing data for early detection of Alzheimer’s disease. Raw nucleotide sequences are preprocessed using one-hot encoding and segmented into uniform lengths, enabling CNNs to learn spatial patterns within genomic sequences that correlate with Alzheimer’s pathology. Our CNN model extracts high-level features from these sequences and performs classification to distinguish between AD-positive and AD-negative samples. Experimental results on publicly available datasets demonstrate the potential of CNNs in achieving high accuracy and robust performance, indicating that deep learning-based sequence analysis can serve as an effective, non-invasive tool for early diagnosis and risk assessment of Alzheimer’s disease. The proposed framework contributes to precision medicine by enabling automated, scalable, and interpretable analysis of genetic information.