Rainfall intensity plays a critical role in shaping environmental outcomes, particularly in climate-sensitive regions like the Mediterranean. Accurate forecasting of rainfall is essential for effective disaster management and climate adaptation strategies, especially as climate change exacerbates the frequency and severity of extreme weather events. This study applies a hybrid model between Decision Tree to extract best meteorological features that has the ability to influence precipitation, and random forest to predict rainfall intensity. The hybrid model classifies the rainfall intensity into three categories: no rainfall, medium rainfall, and high rainfall. Furthermore, the study investigates the influence of key meteorological attributes on rainfall intensity, identifying the most significant variables and their impact. The model demonstrates good performance, achieving an accuracy 0.90, a low Mean Squared Error (MSE) of 0.09, and an Area Under the Curve (AUC) of 0.97. These results underscore the reliability of hybrid index in rainfall prediction and its potential for integrating meteorological insights into climate-sensitive planning and decision-making.