A multimodal machine learning model integrating CT radiomics, body composition, and clinical data predicted neoadjuvant chemotherapy response in locally advanced gastric cancer with AUCs reported between 0.84 and 0.89 across validation cohorts. The approach suggests a non-invasive way to forecast response before treatment and may support risk-stratified trial design and therapy selection.