Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects

Artificial intelligence (AI) is reshaping genomics by enabling unprecedented insights into disease mechanisms, therapeutic design, and precision medicine. This review provides a comprehensive survey of cutting-edge AI methodologies, including machine learning, deep learning (DL), natural language processing, large language models, generative frameworks, and explainable AI, and their applications across genomics. We systematically summarize how these technologies advance key domains, such as gene sequencing, variant detection, gene expression analysis, personalized medicine, and CRISPR-based genome editing. Core computational tools, benchmark datasets, and open-source frameworks supporting AI-driven genomic research are detailed. Despite remarkable progress, challenges persist in data quality, interpretability, ethical governance, and computational scalability. Integrating multi-omics data through advanced architectures, such as graph neural networks and multimodal DL promises deeper biological understanding. Emerging paradigms, e.g. synthetic genomics and digital twins, highlight AI's potential to deliver predictive and personalized healthcare.