Abstract: Advances in computing and the availability of massive health data are opening up new possibilities for the generation of helpful decision-support tools. Forecasting the incidence of medical cases, which is one of the first steps in institutional planning, plays an important role in planning health control strategies in order to develop intervention programs and allocate resources. This study focused on medical cases forecasting for the development of resource allocation recommender system. Data cleaning was performed in the historical data of medical cases from Mabalacat City Health Office in order to detect and removing corrupt and inaccurate records. The forecasting models used are Seasonal Auto-Regressive Integrated Moving Average (S-ARIMA) and Exponential Smoothing (ES). Factor values of twelve (12) for monthly seasonality and four (4) for quarterly seasonality were used for the S-ARIMA models. The alpha values used in ES are 0.1, 0.3, 0.5, 0.7 and 0.9. The computed Mean Absolute Deviation (MAD) and the Mean Absolute Percent Error (MAPE) results of S-ARIMA and ES were compared and the forecasting model with the better accuracy was used for a particular medical case forecast value. The use of the mentioned forecasting algorithms and accuracy tests were embedded in the development of an online information system with resource allocation recommender for Mabalacat City health units.