Forecasting the prices of RON97 fuel is crucial for economic planning and policy-making due to its impact on transportation costs and inflation. This study aims to analyse the RON97 fuel prices and develop a forecasting model using Box-Jenkins modelling. The study employs a comprehensive historical weekly dataset of RON97 fuel prices in Malaysia from 30th March 2017 to 18th April 2024 obtained from Malaysia’s Official Open Data Portal. This research focuses on Stage I and Stage II of the Box-Jenkins approach. Stage I involves model identification, including a preliminary assessment of the data's stationarity through differencing and unit root tests. In Stage II, model estimation is applied to identify the most significant Box-Jenkins model to forecast the fuel price. This stage also includes identifying potential Box-Jenkins models based on autocorrelation functions (ACF) and partial autocorrelation functions (PACF). In this study, the analysis is done with the aid of R software, where the potential of this software in forecasting weekly RON97 fuel prices time series data is explored. The result from the analysis revealed that ARIMA (0,1,2) is the best model for forecasting RON97 fuel prices in Malaysia. Box-Jenkins modelling effectively captures the underlying patterns and trends in RON97 fuel prices, highlighting its applicability in economic and financial time series forecasting.