This study uses time series and machine learning techniques to forecast Saudi Arabia’s refined petroleum output; a significant player in the global energy market. Using data from 1962 to 2022 acquired from the Ministry of Energy, Kingdom of Saudi Arabia, the study evaluates the forecasting performance of different models such as Facebook Prophet, Long Short-Term Memory (LSTM), Gaussian Process (GP), and Auto-Regressive Integrated Moving Average Model(ARIMA) based on metrics including Root mean squared error (RMSE), mean absolute percentage error (MAPE), relative absolute error (RAE), Akaike Information Criterion (AIC), and training time. The study results demonstrate that traditional time series models like ARIMA consistently exhibit superior prediction accuracy, whereas machine learning models like LSTM and GP offer more flexibility but need more data. Conversely, Prophet Model performs poorly as it often overlooks complex patterns within the data. The finding of this research work highlights the need for appropriate methodology use and careful model selection in predictive modeling initiatives to provide decision-makers with relevant information in the energy business. Future research may look into ways to use ensemble modeling techniques and other exogenous factors to increase the accuracy of forecasts for petroleum production.