Machine learning algorithms have been widely used in various prediction applications, such as weather forecasting and disease prediction. This paper focuses on using different types of regression models to predict the future price of Ethereum. The data for the past year, ranging from 2021 to July 12, 2024, has been sourced from Yahoo Finance for training and testing various regression models, including Linear Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Gradient Boosting Regression. Each regression model is trained using 80% of the data for training and the remaining 20% for testing. The performance of each model is evaluated using key performance measures: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R² Score, Explained Variance, and Mean Absolute Percentage Error (MAPE). Among the models, the Random Forest Regression model demonstrated superior performance across all key metrics, particularly on the test set. It achieved the lowest errors (RMSE and MAE), highest R², highest explained variance, and lowest MAPE. These results indicate that the trained Random Forest model is highly effective and capable of accurately predicting the future price of Ethereum.