The rapid growth of bike-sharing systems in urban environments necessitates efficient operations to meet user demand and optimize resource allocation. This article examines the forecasting methods employed by Citi Bike in New York City to enhance operational efficiency. By leveraging time series analysis, machine learning algorithms, and real-time data, Citi Bike can predict demand patterns, optimize bike redistribution, and improve user satisfaction.