The paradigm of oncology is undergoing a fundamental shift from a one-size-fits-all approach towards precision medicine, which seeks to tailor diagnostic and therapeutic strategies to the unique molecular characteristics of an individual's tumor [1]. Genomics has been the cornerstone of this revolution, enabling the identification of driver mutations and facilitating the development of targeted therapies [2]. However, the persistent challenges of intra-tumoral heterogeneity, clonal evolution, and therapeutic resistance have underscored the limitations of a purely genomic viewpoint [3]. The genome represents a static blueprint, and its functional output is dynamically regulated through multiple layers of biological complexity. This recognition has catalyzed the emergence of multi-omics, a holistic approach that integrates data from various molecular layers, including the transcriptome, proteome, and metabolome [4]. This systematic review synthesizes the current status and future perspectives of integrating these multi-omics approaches for advancing precision oncology. We detail how each omics layer—genomics, transcriptomics, proteomics, and metabolomics—contributes unique and complementary insights into tumor biology. We then focus on the synergistic power of their integration, which provides a systems-level understanding capable of deciphering intricate tumor phenotypes, predicting therapy response and resistance, and identifying novel biomarkers [5]. Despite the significant promise, substantial challenges remain in data integration, computational analysis, standardization, and clinical implementation [6]. The future of precision oncology hinges on overcoming these hurdles through the development of robust bioinformatic tools, the validation of multi-omics biomarkers in large-scale prospective trials, and the translation of these sophisticated approaches into routine, actionable clinical practice [7]. The ultimate goal is the construction of a dynamic, multi-dimensional molecular atlas for each patient, paving the way for truly personalized and predictive cancer care.]8[