The *BMC Methods* Collection "Artificial Intelligence for Omics Data Analysis" highlights the integration of artificial intelligence (AI) techniques in the analysis of multi-omics data to advance personalized medicine, disease diagnostics, drug development, and biological pathway elucidation. Recent technological advancements have significantly increased the generation of high-throughput biological data, necessitating more sophisticated methods for interpretation. AI, particularly machine learning (ML) and deep learning (DL), has emerged as a powerful tool to handle the complexity and heterogeneity of multi-omics data, offering insights that traditional statistical methods alone cannot provide. Key AI approaches, such as genomic language models (gLM) and MethylBoostER, have demonstrated effectiveness in various applications, including gene sequence-structure-function relationships and renal tumor subtyping. However, challenges remain, including data heterogeneity, interpretability, overfitting, the curse of dimensionality, computational costs, and the need for FAIR solutions. The collection emphasizes the importance of addressing these challenges to ensure the reliable and reproducible use of AI in biomedical research. The editorial team, comprising experts from various institutions, underscores the potential of AI in advancing personalized medicine and highlights the need for further research and collaboration to overcome current limitations. They invite contributions from researchers to advance the field of AI-driven omics data analysis and its applications in biological and medical research.The *BMC Methods* Collection "Artificial Intelligence for Omics Data Analysis" highlights the integration of artificial intelligence (AI) techniques in the analysis of multi-omics data to advance personalized medicine, disease diagnostics, drug development, and biological pathway elucidation. Recent technological advancements have significantly increased the generation of high-throughput biological data, necessitating more sophisticated methods for interpretation. AI, particularly machine learning (ML) and deep learning (DL), has emerged as a powerful tool to handle the complexity and heterogeneity of multi-omics data, offering insights that traditional statistical methods alone cannot provide. Key AI approaches, such as genomic language models (gLM) and MethylBoostER, have demonstrated effectiveness in various applications, including gene sequence-structure-function relationships and renal tumor subtyping. However, challenges remain, including data heterogeneity, interpretability, overfitting, the curse of dimensionality, computational costs, and the need for FAIR solutions. The collection emphasizes the importance of addressing these challenges to ensure the reliable and reproducible use of AI in biomedical research. The editorial team, comprising experts from various institutions, underscores the potential of AI in advancing personalized medicine and highlights the need for further research and collaboration to overcome current limitations. They invite contributions from researchers to advance the field of AI-driven omics data analysis and its applications in biological and medical research.