In recent years, the gold market has witnessed one of the most volatile gold price eras ever. For decades, developing an accurate gold price prediction model has been a complex problem for researchers. Within the scope of this research, we apply several machine learning methods to the problem of forecasting time series data using a combination of three models, including Long short-term memory (LSTM) - Convolutional Neural Network (CNN) - Random Forest Regression (RF) to forecast future gold prices. Two performance metrics, including Mean Absolute Error (MAE) and Mean Squared Error (MSE), are used to evaluate the performance of the different models developed. Experimental results show that the proposed Random Forest model has outstanding performance in predicting and changing trends of gold prices in both the short and long term, with the lowest MAE and MSE of all three models. In general, RF has improved prediction accuracy, which is also a potential bright spot for building an increasingly optimal prediction model to forecast gold prices and contribute to forecasts in other fields.