Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC's heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in precision diagnosis, prognosis prediction, and biomarker discovery for achieving personalized medicine.