Cancer is a leading cause of mortality, with ten million deaths annually. This study focuses on classifying gastrointestinal (GI) cancers—esophageal, liver, colon, stomach, and pancreatic—using symptomatology and gene expression data. An advanced machine learning (ML) model combining SVM, RF, DT, and LR demonstrated superior accuracy compared to ten other ML algorithms. The findings highlight ML's potential to enhance diagnostics, offering precise and effective medical interventions.