Multi-omics integration has emerged as a transformative approach in both bioinformatics and cheminformatics, providing a deeper understanding of biological systems by bridging various molecular data types, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. This chapter offers a comprehensive overview of multi-omics technologies and discusses integration strategies, providing insights into both horizontal integration—combining data from the same omics type across different conditions—and vertical integration, which links different omics layers to uncover complex molecular interactions. The chapter begins by outlining key omics technologies and their respective contributions to systems biology and chemical biology. It explores the significance of multi-omics integration in mapping the flow of molecular information from genes to proteins and metabolites, highlighting its applications in disease biomarker discovery, personalized medicine, drug target identification, and metabolic pathway reconstruction. Special attention is given to the relevance of multi-omics data in cheminformatics, underscoring its role in facilitating drug discovery, optimizing therapeutic interventions, and advancing the understanding of chemical-biological interactions. Furthermore, the chapter addresses major challenges in multi-omics integration, such as data heterogeneity, high dimensionality, and the need for interpretable results. Strategies to address these challenges, including advanced machine learning techniques and artificial intelligence (AI), are also discussed. The chapter concludes with a forward-looking perspective on emerging technologies and the future of multi-omics integration in both bioinformatics and cheminformatics.