Aim of this study is to identify diet and lifestyle patterns that cause microsatellite instability gastric cancer (MSI-GC) using supervised machine learning algorithms. There were 142 genetic variants acquired via targeted resequencing of 60 biomarker genes from gastric tumor samples and tabulated with respect to MSI status, diet and lifestyle characteristics. Four classifiers (logistic regression, random forest, logistic regression, multilayer perceptron) were used to train the data and evaluated based on their classification efficiency. Data analysis revealed features extracted using ridge regression: extra salt, smoked food, smokeless tobacco products (Khaini /sadha), alcohol and betel nut leaf with lime (khuva) were the core factors for causing MSI-GC. The extracted features were exploited using random forest and multilayer perceptron classifiers, which has produced accuracy, precision, recall, F1 score, and Receiver operating characteristics (ROC) curve of 96 %. The brier score was 0.04 and Matthews correlation coefficient (MCC) was +0.91. Linear regression results revealed khuva was main driving factor and extra salt, smoked food, khaini/sadha and alcohol were confounding factors to cause MSI-GC. This is a first-time report that integrates mutation and diet-lifestyle data using machine learning, to precisely identify the driving and confounding factors for causing MSI-GC.