This dataset provides the Python implementation, GEE workflow, and trained Random Forest models used for agricultural and hydrological drought assessment in Ethiopia from 1982–2100. The work integrates ERA5-Land, FLDAS, CHIRPS, CHIRTS, and multi-model CMIP6 datasets (SSP245, SSP585) to analyze past and future drought dynamics.The Random Forest model used for prediction of agricultural (SSMI/based) and hydrological (SRI-based) drought indices is openly available for reuse and adaptation.