This study applies a time series approach to analyze and forecast blood demand using the ARIMA model. The analysis begins with exploratory data analysis to understand the characteristics of the data, followed by stationarity testing and model identification based on ACF and PACF. Several ARIMA models are evaluated using training data, and model performance is assessed on testing data using Mean Absolute Percentage Error (MAPE). The results demonstrate that ARIMA can be used as a practical approach for modeling and forecasting blood demand based on historical data.