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The RTA Metro system plays a crucial role in urban mobility in Dubai, offering a sustainable and affordable means of transportation for millions of people weekly. However, this system often faces challenges in providing reliable and timely services, leading to inconveniences and dissatisfaction among commuters. In light of these issues, this paper presents a comparative time series analysis for demand forecasting, aiming to identify the most accurate and reliable forecasting methods. The analysis was conducted using historical ridership data from Dubai Metro, spanning several months. By leveraging benchmark models for time series forecasting, such as LSTM and GRU, we observed promising results, indicating the potential for accurate prediction of passenger demand patterns in the Dubai Metro system. Although further optimization of forecasting models can be done, the outcomes of this project lay a solid foundation for enhancing passenger demand forecasting in urban rail transit systems.