Purpose of Review <p>Atherosclerosis (AS) is a progressive disease of the arterial wall characterized by metabolic dysregulation, inflammatory activation, and genetic susceptibility. Given the complex interactions across molecular layers, this review aims to summarize the key applications of multi-omics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, single-cell omics, spatial omics, plasma proteomics, and radiomics, in elucidating AS pathogenesis and clinical relevance.</p> Recent Findings <p>Recent multi-omics studies have enabled the construction of functional networks linking genetic variation, epigenetic regulation, gene expression, protein function, and metabolic imbalance, thereby providing complementary insights into AS mechanisms. These approaches have advanced the understanding of distinct pathological phenotypes, such as calcified versus non-calcified plaques and stable versus unstable lesions. Emerging evidence also highlights the clinical relevance of underexplored areas, including molecular subtyping, and plasma biomarker prediction. Furthermore, the integration of artificial intelligence (AI) has enhanced multi-omics data mining, particularly in radiomics-based phenotypic profiling and multidimensional risk modeling.</p> Summary <p>This review synthesizes current advances in multi-omics strategies for AS research and discusses the sources and application status of human samples in representative studies, emphasizing differences in acquisition methods, utilization rates, and omics preferences across vascular beds. Collectively, these integrative approaches support systems biology frameworks and hold promise for informing precision strategies for early detection, risk stratification, and targeted intervention in AS.</p>