Advances in high-throughput sequencing (HTSHigh-Throughput Sequencing (HTS)) have transformed genomicsGenomics into a data-intensive domain, presenting challenges such as extreme dimensionality, high sparsity, and complex correlation structures. This chapter explores the application of artificial intelligence (AIArtificial Intelligence (AI)) methodologies in genomic analysisGenomic analysis, ranging from classical machine learningClassical machine learning techniques to deep learningDeep Learning (DL) architectures, transformerTransformers-based foundation modelsFoundation models, and generative AIGenerative AI. Classical machine learningClassical machine learning methods, such as logistic regression, support vector machines, and random forests, offer interpretable and computationally efficient solutions for genomic predictionGenomic prediction and classification tasks. However, their limitations in modeling complex interactions and long-range dependencies have led to the adoption of deep learningDeep Learning (DL) approaches, including convolutional neural networks (CNNsConvolutional Neural Networks (CNNs)), recurrent neural networks (RNNsRecurrent Neural Networks (RNNs)), and attention-based modelsAttention-based models. TransformersTransformers and foundation modelsFoundation models have further revolutionized genomic analysisGenomic analysis by enabling self-supervised learning, multi-modal integration, and long-range dependency modeling. Generative AIGenerative AI has opened new avenues for synthetic biologySynthetic biology, allowing the design of novel DNA, RNA, and protein sequences with desired biological functions. The chapter also emphasizes the importance of robust model evaluation, bias mitigationBias mitigation, and interpretability to ensure the responsible application of AIArtificial Intelligence (AI) in genomicsGenomics. Future directions include scalable sequencing analysis, multi-omicsMulti-omics integrationMulti-omics integration, privacy-preserving techniques, and ethical considerationsEthical considerations, highlighting the transformative potential of AIArtificial Intelligence (AI) in genomicGenomics research and personalized medicine.