In medical terminology, cancer is a group of disorders which can spread to various organ sections and are caused by abnormal proliferation. Among all diseases, cancer ranks second in mortality after cardiovascular diseases, according to the WHO. A valuable and comprehensive resource for researchers in the fields of genomics and cancer is the Gene Expression Omnibus (GEO). An extensive collection of gene expression profile datasets is available, enabling one to investigate the transcriptome profiles associated with various types of cancer. Gene expression can be used to detect cancer in its early stages by reflecting both genetic values and biochemical activities. The use of DNA microarrays and RNA sequencing techniques can be used to quantify gene expression data. XGBoost and Naive Bayes, Support Vector Machine, Random Forest, are examined for their effectiveness in correctly categorizing cancer subtypes using gene expression data for the classification of gastric cancer datasets. Using public datasets and difficult evaluation metrics, the study provides meaningful inferences, based on algorithm performance, around 70–90% accurate.