The manuscript proposes a novel SHapley Additive exPlanations (SHAP) based ensemble framework that explains the working of a stacking ensemble classifier. The considered stacking ensemble classifier comprises the three base learners chosen by their accuracy of validation. The method ranks the candidate base models and then selects the three topmost performing models to create a stacking classifier that captures complementary inductive biases. It then uses model-agnostic SHAP on the ensemble’s probability output to explain the entire ensemble model. This gives multilevel transparency to the user by incorporating global feature importance through mean SHAP and class-wise summaries that show unique drivers to certain classes. The current research also demonstrates per-instance waterfall graphs that show how each decision was made. The pipeline is easy to replicate and works with binary as well as multiclass tabular data. While stacked ensemble aims to increase the accuracy, precision, and recall over its parts; ensemble SHAP shows the most important predictors and flags unusual cases for audit aiding its safe deployment.