Recent studies in the Philippines on landslides have primarily focused on susceptibility mapping and the generation of hazard maps. However, research on landslide forecasting remains less explored. As artificial intelligence continues to progress, forecasting methods used have also become more advanced. This study focuses on rainfall, a primary triggering factor of landslides, examining its relationship with environmental variables such as slope, soil type, and soil moisture to predict potential landslide events. The researchers applied the random forest model to forecast landslides using a minimized yet significant set of predictors (rainfall, slope, soil moisture, slope). The data of these variables have been gathered from various sources, such as sites with real-time data and government agencies that provide public datasets. These were then added into the dataset inventory created by the researchers and then trained using the random forest model. One-way ANOVA was then conducted to assess differences in model performance under various combinations of input variables, followed by post hoc tests to determine the most effective predictive variables. The researchers found that combining rainfall and environmental variables as predictors yielded the highest accuracy at 90%, outperforming models that used only individual variable inputs. These findings demonstrate the effectiveness of the random forest model in forecasting landslides even with limited resources and data, highlighting its potential as a practical and adaptable tool for early warning systems in areas such as the mountainous regions of the Philippines.