Alzheimer’s disease prediction using genomic data remains challenging due to the high dimensionality of whole-genome sequencing data and the complex relationships between genetic variants. We developed DuAL-Net (Dual Approach Local-global Network), a hybrid framework that integrates local genomic window analysis with global annotation-based modeling to prioritize disease-associated single-nucleotide polymorphisms (SNPs). As a proof of concept, we applied DuAL-Net to 14,094 SNPs within the APOE ±50-kb region from 1,050 individuals in the Alzheimer’s Disease Neuroimaging Initiative and Alzheimer’s Disease Sequencing Project (ADSP) cohorts. Using nested 5-fold cross-validation, DuAL-Net achieved an area under the receiver operating characteristic curve (AUC) of 0.698 (95% confidence interval: 0.659 to 0.737) for the top 100 ranked SNPs, substantially outperforming bottom-ranked SNPs (AUC = 0.479). Validation on an independent ADSP Alzheimer’s Disease Centers cohort (n = 5,570) confirmed generalizability, with top-ranked SNPs achieving AUC = 0.686 versus 0.516 for bottom-ranked SNPs. The framework successfully identified established risk variants, including rs429358 and rs7412, validating its ability to prioritize biologically relevant SNPs. DuAL-Net provides a generalizable approach for integrating local and global genomic information in Alzheimer’s disease risk prediction.