Diagnostic pathology has undergone a remarkable transformation from a predominantly morphology-based discipline to an integrated field that combines histopathology, immunohistochemistry, molecular pathology, and computational biology. The emergence of multi-omics approaches—including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and spatial omics—has enabled comprehensive characterization of disease biology by providing complementary molecular insights. These technologies have significantly advanced precision medicine by improving disease classification, facilitating early and accurate diagnosis, identifying clinically actionable biomarkers, predicting therapeutic response, and uncovering mechanisms of treatment resistance. Furthermore, the integration of multi-omics data through advanced computational and artificial intelligence–driven analytical methods has enhanced diagnostic accuracy and personalized clinical decision-making. This review provides a comprehensive overview of current multi-omics technologies, their applications in diagnostic pathology, computational integration strategies, clinical significance, existing challenges, and future directions for their implementation in precision diagnostics and personalized medicine.