To study and better understand complex biological processes, it is crucial to take an integrative approach that combines multi-omics data including genomes, epigenomes, transcriptomes, proteomes, and metabolomes to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and the availability of multi-omics data generated from a large set of samples, several tools and methods have been developed for data integration and interpretation. This seminar provides definitions, applications, and challenges associated with multi-omics data integration and briefly reviews some machine learning models that adopt an integrative approach to analyze multiple omics data.