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Malaria poses a significant health challenge globally, including in Malaysia, caused by Plasmodium parasites and is capable of causing severe complications or death. Sabah has consistently experienced high malaria cases from 2018 to 2021. This study aims to identify the most suitable ARIMA model for forecasting malaria cases in Sabah from 2022 to 2024. The Box-Jenkins Methodology was utilized to achieve this objective, using weekly malaria case data in Sabah from January 2018 to December 2021. Four ARIMA models (ARIMA (1,1,1), ARIMA (1,1,2), ARIMA (2,1,1), and ARIMA (3,1,1)) were specified and assessed based on Akaike’s Information Criteria (AIC), Bayesian Information Criterion (BIC), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The results indicate that ARIMA (2,1,1) is the most appropriate model for forecasting malaria cases in Sabah. The forecast suggests a slight increase in malaria cases from January 2022 to December 2024, with 5.09% and 7.27% increments, respectively. This reflects a gradual rise in malaria incidence. This study will be beneficial in terms of precise prediction and early detection of malaria cases which are the main factors in the containment of this disease. Additionally, the forecast research aids in allocating healthcare resources effectively, directing them to areas with the greatest need. This is particularly valuable for states or countries with limited healthcare access, enabling them to be more prepared for malaria outbreaks at unexpected times.