The main focus of this paper is using ensemble methods to estimate the ground state energies of molecules. In the first part we analyze different literatures that focus on issues that are encountered when predicting ground state energies of molecules. Dataset which is used in this paper is created by using basic information about molecules from PubChem database. First we create a few modified versions of dataset using various scaling methods such as: Standard Scaler, Power Transformer, Normalizer, Robust Scaler, MinMax Scaler and MaxAbs Scaler. These modified datasets will be used to train ensemble algorithms and we want to observe the impact that different scaling methods have on original dataset and how that reflects onto the precision of ground state energy estimations. The ensemble methods used in this paper are: Decision Tree, Random Forest, Extra Tree, AdaBoost, HistGradientBoost, XGBoost, Bagging, Voting and Stacking regressors. The training for each of these methods is performed on every modified dataset. The training is done by using cross-validation and finding optimal hyperparameters. To find optimal hyperparameters two functions are used: GridSearchCV and RandomizedSearchCV. The main objectives are to determine the impact that number of folds in cross-validation have on the model accuracy and to determine if we can improve the model accuracy by combining multiple basic ensembles into one ensemble. To compare the ensemble methods we use the following evaluation metrics: R2, MAE, MAPE, MSE, RMS and KGE. The second part of the paper focuses on analyzing the acquired results. By comparing evaluation metrics for each of the ensemble methods used we come to a conclusion that XGBoost, Voting and Stacking regressors perform the best and that by combining multiple ensemble methods into one ensemble we can increase the model performance. When analyzing the impact that number of folds have on the accuracy of the model we came to a conclusion that the higher number of folds doesn’t automatically result in higher model accuracy, which means that when using cross-validation it is necessary to find the optimal number of folds to maximize model performance. Lastly we compare the best ensemble methods and explain their strengths and weaknesses and offer an idea to improve the results of ensemble methods by using them in a hidden layer of an artificial neural network (ANN).