The COVID-19 pandemic has been a global health crisis since December 2019, when the first infection was reported in Wuhan, China. The critical and lethal advancement of this disease is associated with the failure of multiple organs including, but not limited to, the brain, lungs, heart, liver, kidneys, etc., which makes it very challenging to understand. Current high-throughput technologies generate multi-omics datasets to enable a comprehensive and in-depth analysis of different organs at the cellular and molecular level. To understand the multi-organ impact of COVID-19 and the mechanistic aspects of disease prognosis and its interactions with other comorbidities, computational approaches need to be implemented by integrating data from multiple organs, correlating results across data types, and applying machine learning (ML) tools on the high-throughput data. This chapter is expected to provide valuable insights to help explain the multi-organ association of COVID-19 using state-of-the-art computational resources and modeling of high-volume data. We have emphasized the importance of big data analytics and systematic integration of data from different domains including omics, clinical, demographic, and others in understanding the organ- and system-level biological processes and comorbidity networks associated with COVID-19. These findings and proposed strategies could help perform comorbidity-focused studies to understand and tackle COVID-19.