This paper aims to offer an improved version of the new composite drought monitoring index (CDMI) and to test its applicability in the context of Tensift watershed in Morocco. A synergistic approach incorporating the remote sensing techniques, hydrometeorological data, simulated data and agricultural statistics was used for this purpose. After assessing the performance of CDMI, estimated Soil Moisture Anomaly Indicator (ISMA) was processed, validated and incorporated to the composite model. Random Forest algorithm was used to determine the weight of composite model components. Apart from comparative mapping, Pearson's correlation statistical analysis, linear regression and dependency tests were used to assess the performance of the improved composite model (CDMIa_RF). The result show that CDMIa_RF is better correlated with several indices such as: the Standardized Precipitation Index (SPI), (R-2=0.74); Hydrological Drought Index (R-2=0.70); grain productivity (R-2=0.70), CDMI (R-2=0.95), Vegetation Health Index (VHI), (R-2=0.87), and Normalized Vegetation Supply Water Index (NVSWI), (R-2= 0.85).