The advent of high-throughput omics technologies poses new challenges in analysing, processing, and merging different sources of information into biological and clinically meaningful contexts. Integrated omics focuses on the computational and informatics frameworks that facilitate the fusion of major omics data types used in everyday biology (including genomics, transcriptomics, proteomics, metabolomics, and phenomics). The term is used interchangeably with multi-omics, pan-omic, and trans-omic approaches. The overarching goal of Integrated omics is to generate a systems biology overview of the biological question, which would provide unprecedented and transformative insights not observed by singular analysis of individual omics platforms. This is an exciting but immense challenge for computational biologists and bioinformaticians. Machine-learning approaches for analysis of integrated omics datasets, and methods than can facilitate cross-talk of multi-omic layers, are used to process and understand the complexity of heterogenous datasets. Part of the strategy includes developing methods that facilitate integrating data of different dimensions and biological contexts, methods data normalization, and development of storage platforms that can handle up to peta-byte sized data files. Ultimately, these efforts provide more comprehensive views of human health, disease, and basic biology.