Cancer, the second leading cause of global mortality, presents a complex challenge necessitating advanced understanding and treatment strategies. This article explores the significance of multi-omics approaches, integrating genomics, epigenomics, transcriptomics, and proteomics, in comprehensively unraveling cancer complexities. Glioma, breast, ovarian, liver, and colorectal cancers are discussed, emphasizing their unique characteristics and the urgent need for computational methods to identify therapeutic targets. The integration of diverse omics data is crucial, with advancements in methods for both bulk and single-cell populations. Challenges in multi-omics fusion analytics include data heterogeneity and high dimensionality. Various computational frameworks, such as iCluster and SNF, are detailed, highlighting their roles in subtyping, biomarker identification, and pathway analysis across cancers. The review introduces databases like DriverDBv3 and LinkedOmics, enhancing the interpretation of cancer omics data through visualizations and comprehensive exploration tools. Additionally, it covers the landscape of metabolomics in cancer research, revealing metabolic signatures for early detection and targeted therapies. Advances in surface proteomics and secretome profiling underscore the evolving landscape of therapeutic strategies, with a focus on tumor surface proteins and innovative platforms for single-cell assessment. Overall, this article provides a comprehensive overview of multi-omics applications, computational methodologies, and emerging technologies in the intricate landscape of cancer research.