Forecasting the gold price movement's volatility has essential applications in areas such as risk management, options pricing, and asset allocation. The multivariate model is expected to generate more accurate forecasts than univariate models in time series data like gold prices. Multivariate analysis is based on observation and analysis of more than one statistical variable at a time. This paper mainly builds a multivariate prediction model based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model to analyze and forecast the price of the gold commodity. In addition, the prediction model is optimized with a Cross-Validated Grid Search to find the optimum hyperparameter. The empirical results show that the proposed Timeseries Prediction model has an excellent accuracy in prediction, that proven by the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Overall, in more than three years data period, LSTM has high accuracy, but for under three years period, GRU does better. This research aims to find a promising methodology for gold price forecasting with high accuracy.