Artificial intelligence (AI) is transforming omics data analysis, enabling more accurate and comprehensive insights into biological systems. The BMC Methods Collection "Artificial intelligence for omics data analysis" highlights novel AI approaches using multi-omics data to accelerate discoveries in personalized medicine, disease diagnostics, drug development, and biological pathway elucidation. Recent technological advancements have significantly improved access to high-throughput biological instrumentation, leading to an unprecedented surge in omics data generation. However, single omics data may not fully explain complex biological phenomena, making multi-omics integration essential. AI techniques, including machine learning (ML) and deep learning (DL), are increasingly used to analyze and interpret omics data, offering more holistic views of biological mechanisms. For example, the genomic language model (gLM) has shown promise in bridging the gap between genomic context and gene function, while MethylBoostER has been effective in differentiating renal tumor subtypes. AI also facilitates the prediction of biological outcomes and accelerates biomedical research. Challenges remain, including data heterogeneity, missing values, overfitting, and the curse of dimensionality. Addressing these issues requires FAIR solutions that ensure data is findable, accessible, intelligent, and reproducible. The integration of AI with multi-omics data holds great potential for advancing personalized medicine and improving public health. The BMC Methods Collection invites researchers to contribute innovative AI approaches in omics data analysis.Artificial intelligence (AI) is transforming omics data analysis, enabling more accurate and comprehensive insights into biological systems. The BMC Methods Collection "Artificial intelligence for omics data analysis" highlights novel AI approaches using multi-omics data to accelerate discoveries in personalized medicine, disease diagnostics, drug development, and biological pathway elucidation. Recent technological advancements have significantly improved access to high-throughput biological instrumentation, leading to an unprecedented surge in omics data generation. However, single omics data may not fully explain complex biological phenomena, making multi-omics integration essential. AI techniques, including machine learning (ML) and deep learning (DL), are increasingly used to analyze and interpret omics data, offering more holistic views of biological mechanisms. For example, the genomic language model (gLM) has shown promise in bridging the gap between genomic context and gene function, while MethylBoostER has been effective in differentiating renal tumor subtypes. AI also facilitates the prediction of biological outcomes and accelerates biomedical research. Challenges remain, including data heterogeneity, missing values, overfitting, and the curse of dimensionality. Addressing these issues requires FAIR solutions that ensure data is findable, accessible, intelligent, and reproducible. The integration of AI with multi-omics data holds great potential for advancing personalized medicine and improving public health. The BMC Methods Collection invites researchers to contribute innovative AI approaches in omics data analysis.