A gas lift well requires precise tuning of its operating parameters to give its production performance, which is a complex process. Conventional approaches encountered significant challenges in resolving the well production prediction with numerous parameters and multiple wells, which could give a considerable load to the production engineer. Machine Learning is an application focused on improving programs that could learn from various operational experiences. Implementing Machine Learning in gas lift operation to predict oil production could provide significant working time reduction since they enable a simulation of the behavior of a gas-lifted well using historical data without modeling the well in commercial software. This paper presents a prediction of gas lift oil production by utilizing some machine learning algorithms, which use the existing data from the Society of Petroleum Engineers (SPE) repository as time series data. Four algorithms, Auto-Regressive Integrated Moving Averages (ARIMAX), Vector Auto Regression (VAR), Long-Short-Term Memory (LSTM), and Prophet were studied, and their results were compared to the actual gas lift well data and commercial software results. The study shows that LSTM was the best model among the other three models indicated by the lowest evaluation metrics: Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 36.33 and 54.36, respectively. The average difference between the LSTM model result from the actual data was 4.8% while the well model resulted in a 9.7% average difference from the actual data. This machine learning model was then used to predict the oil production of another well in the range of the first well oil production data, resulting in a relatively low average difference of 4% from the actual produced oil data.