Accurately predicting and modeling the total electron content (TEC) of the ionosphere can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study tested the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data from a single station in Addis Ababa, Ethiopia. The models used were gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial six months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of $\sim$0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the GBDT-based algorithms and their stacked integration demonstrated promising potential for predicting VTEC over Addis Ababa, Ethiopia.