Developing fuel resources is strategically crucial for Armenia. Far more than any other fossil fuel resource, coal roughly generates half the nation’s electricity. Although coal could play a critical role, no vast data is available about Armenia coal properties. Using robust modeling of energy indexes such as coal gross calorific value (GCV) by considering trivial existing datasets could be an essential clue for ensuring sustainable development. For the first time, this investigation is going to model GCV for Armenia coal samples. For this purpose, SHAP (SHapley Additive exPlanations) as a novel explainable artificial intelligence will be introduced. SHAP enables understanding the magnitude of relationships between each individual input record and its representative output and ranks input variables based on their effectiveness. SHAP was coupled by extreme gradient boosting (xgboost) as the most recently generated powerful predictive machine learning tool (SHAP-Xgboost). SHAP-Xgboost could accurately (R²=0.99) model GCV based on proximate and ultimate variables of Armenia coal samples. These significant outcomes open a new window for developing high interpretability models to assess coal properties and pinpoint the influential parameters.