Abstract
<jats:p>Genomics has emerged as a cornerstone of modern biomedical research, enabling the comprehensive study of genome structure, function, and variation across organisms. With the advent of high-throughput sequencing technologies and the accumulation of large-scale omics data, AI, ML, and DL have become indispensable in complex genomic analysis. The integration of AI across the broad spectrum of genomic disciplines, including structural and functional genomics, and specialized subfields such as epigenomics, pharmacogenomics, metagenomics, and cancer genomics, is explained. In structural genomics, AI models have accelerated disease diagnosis and drug discovery, predicting gene mutations and mapping genome architecture. Genomics powers AI, biomarker discovery, microarray analytics, and genome editing. AI facilitates predictive medicine, disease surveillance, streamlining treatment protocols, and augmenting patient care. The integration of AI in genomics is enhancing diagnostic precision and therapeutic strategies, shaping the future of personalized and population-scale healthcare.</jats:p>