The integration of multi-omics datasets including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and metagenomics has revolutionized modern biomedical research by enabling systems-level understanding of biological processes and personalized medicine. However, the size, heterogeneity, and sensitivity of these data far exceed the capacity of traditional computing systems. Cloud computing provides scalable and flexible resources for storage, processing, and sharing of massive datasets, enabling real-time analytics and collaborative research. This review discusses the architecture, workflow systems, data integration strategies, privacy-preserving mechanisms, and reproducibility frameworks that underpin cloud-based multi-omics data analytics. It also examines major challenges, emerging trends such as federated learning and AI-based integration and offers future perspectives for global-scale bioinformatics infrastructure.