This chapter explores the transformative intersection of genomicsGenomics and artificial intelligence (AIArtificial Intelligence (AI)), emphasizing the shift from traditional geneticsGenetics Artificial Intelligence (AI) to genomicsGenomics and the critical role of AIArtificial Intelligence (AI) in modern biological research. Genetics focuses on individual genes and their inheritance, while genomicsGenomics examines the entire genome, including complex interactions between genes and environmental factors. The chapter highlights the central dogmaCentral Dogma ofCentral Dogma models molecular biology, gene architecture, regulatory elements, and the evolution of DNA sequencingDNA sequencing technologies, from Sanger sequencing to next-generation and long-read sequencingLong-read sequencing. It discusses the challenges posed by the explosion of genomic dataGenomic data and the computational bottlenecks that necessitate GPU-accelerated AIArtificial Intelligence (AI) solutions. The chapter also delves into the evolution of AIArtificial Intelligence (AI) in genomicsGenomics, from classical machine learningClassical machine learning to deep learningDeep Learning (DL), and the application of advanced neural architectures such as CNNsConvolutional Neural Networks (CNNs), RNNsRecurrent Neural Networks (RNNs), transformersTransformers, and Graph Neural NetworksGraph Neural Networks (GNNs). Ethical considerationsEthical considerations, including bias in training data and privacy concerns, are addressed, along with the future potential of generative biologyGenerative biology and AIArtificial Intelligence (AI)-driven drug discoveryDrug discovery. This chapter underscores how AIArtificial Intelligence (AI) is reshaping genomicsGenomics into a data-driven science, enabling groundbreaking discoveries and innovations in understanding and manipulating the code of life.