COVID-19 is now one of the leading causes of mortality amongst adults TRANS in the United States for the year 2020. Multiple epidemiological models have been built, often based on limited data, to understand the spread and impact of the pandemic. However, many geographic and local factors may have played an important role in higher morbidity and mortality in certain populations. The goal of this study was to develop machine learning models to understand the relative association of socioeconomic, demographic, travel TRANS, and health care characteristics of different states across the United States and COVID-19 mortality. Using multiple public data sets, 24 variables linked to COVID-19 disease MESHD were chosen to build the models. Two independent machine learning models using CatBoost regression and random forest were developed. SHAP feature importance and a Boruta algorithm were used to elucidate the relative importance of features on COVID-19 mortality in the United States. Feature importances from both the categorical models, i.e., CatBoost and random forest consistently showed that a high population density, number of nursing homes, number of nursing home beds and foreign travel TRANS were strongest predictors of COVID-19 mortality. Percentage of African American amongst the population was also found to be of high importance in prediction of COVID-19 mortality whereas racial majority (primarily, Caucasian) was not. Both models fitted the data well with a training R2 of 0.99 and 0.88 respectively. The effect of median age TRANS,median income, climate and disease MESHD mitigation measures on COVID-19 related mortality remained unclear. COVID-19 policy making will need to take population density, pre-existing medical care and state travel TRANS policies into account. Our models identified and quantified the relative importance of each of these for mortality predictions using machine learning.