Gastric cancer (GC) remains a major global health challenge, particularly in its advanced stages where prognosis is poor, and treatment responses are heterogeneous. Precision oncology aims to tailor therapies, but current biomarkers have limitations. Artificial Intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers powerful tools to analyze complex, multi-dimensional data from advanced GC patients, including clinical records, genomics, imaging (radiomics), and digital pathology (pathomics). This review synthesizes the current state of AI applications in unresectable, advanced GC. AI models demonstrate significant potential in refining diagnosis and staging, predicting treatment efficacy for chemotherapy, immunotherapy, and targeted therapies, and assessing prognosis. Multi-modal AI approaches, integrating data from diverse sources, consistently show improved predictive performance over single-modality models, better reflecting the complexity of the disease. Key challenges remain, including data quality and standardization, model generalizability and interpretability, and the need for rigorous prospective validation. Future directions emphasize multi-center collaborations, development of robust and explainable AI (XAI), and seamless integration into clinical workflows. Overcoming these hurdles will be crucial to translate AI's potential into tangible clinical benefits, enabling truly personalized and effective management for patients with advanced gastric cancer.