In recent decades, machine learning (ML) algorithms has gained wide popularity in the finance literature. The goal of this research is to exploit machine learning techniques in order to analyze the effect of exchange-traded fund (ETF) illiquidity on tracking errors. We demonstrate the superior performance of the machine learning models ? Random Forest and Gradient Boosting Decision Tree, in particular - over traditional linear models in predicting U.S. ETF’s tracking errors. Moreover, our variable importance analysis suggests that the features such as underlying assets based on U.S. assets (Invested in US Asset) and expense ratio (Expense Ratio), are two key factors in the determination of predicting the tracking errors on the ETF illiquidity. Finally, we further conduct SHAP (Shapley Additive exPlanations) technique in order to observe the impact of a particular variable(feature) on the difference between the considered- and average predictions of our machine learning models. Our results indicate that the most relevant variable is Invested in US Asset, which is in align with the previous importance analysis.