Time series analysis plays a vital role in extracting meaningful information from temporal data. Forecasting Time series data is a powerful tool for businesses to make informed decisions, improve efficiency, and stay ahead of the competition. This study aims to fit an appropriate model for the Air traffic data. The data series exhibited cyclical patterns and/or seasonality. Both of these properties were taken into account in the time series models utilized in this study. In the analysis of this data, both smoothing methods and ARIMA models were used. Since seasonality is a complicated factor in a time series, findings inferred that SARIMA models perform well. Based on the accuracy measures we could infer that the models offer a promising means of forecasting air passenger demand.