Highlights What are the main findings? A hybrid Random Forest-LSTM model improves drought forecasting by using RF to identify the most critical climate predictors and LSTM to model their temporal evolution. The Yellow River Basin is projected to face a rapid intensification of drought severity and frequency post-2040, especially under the high-emission SSP5-8.5 climate scenario. What is the implication of the main finding? The hybrid AI approach provides a powerful and replicable framework for developing more reliable seasonal and long-term drought early warning systems. The findings directly support anticipatory water resource management by quantifying future drought risks and informing climate adaptation strategies in critical water-scarce regions.Highlights What are the main findings? A hybrid Random Forest-LSTM model improves drought forecasting by using RF to identify the most critical climate predictors and LSTM to model their temporal evolution. The Yellow River Basin is projected to face a rapid intensification of drought severity and frequency post-2040, especially under the high-emission SSP5-8.5 climate scenario. What is the implication of the main finding? The hybrid AI approach provides a powerful and replicable framework for developing more reliable seasonal and long-term drought early warning systems. The findings directly support anticipatory water resource management by quantifying future drought risks and informing climate adaptation strategies in critical water-scarce regions.Abstract Droughts are increasingly threatening ecological balance, agricultural productivity, and socio-economic resilience-especially in semi-arid regions like the Inner Mongolia segment of China's Yellow River Basin. This study presents a hybrid drought forecasting framework integrating machine learning (ML) and deep learning (DL) models with high-resolution historical and downscaled future climate data. TerraClimate observations (1985-2014) and bias-corrected CMIP6 projections (2030-2050) under SSP2-4.5 and SSP5-8.5 scenarios were utilized to develop and evaluate the models. Among the tested ML algorithms, Random Forest (RF) demonstrated the best trade-off between accuracy and interpretability and was selected for feature importance analysis. The top-ranked predictors-precipitation, solar radiation, and maximum temperature-were used to train a Long Short-Term Memory (LSTM) network. The LSTM outperformed all ML models, achieving high predictive skill (R2 = 0.766, CC = 0.880, RMSE = 0.885). Scenario-based projections revealed increasing drought severity and variability under SSP5-8.5, with mean PDSI values dropping below -3 after 2040 and deepening toward -4 by 2049. The high-emission scenario also exhibited broader uncertainty bands and amplified interannual anomalies. These findings highlight the value of hybrid AI-climate modeling approaches in capturing complex drought dynamics and supporting anticipatory water resource planning in vulnerable dryland environments.