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Reliable projections of hydrological drought are essential for climate-resilient water management; however, many basins lack calibrated, process-based models. Here, we develop and test a purely data‑driven framework to forecast the Streamflow Drought Index (SDI) for the Sava River basin, using only widely available meteorological drought indices, and apply it to project future drought conditions under different climate scenarios. We assembled a monthly dataset for 1961–2020 comprising the Standardized Precipitation Index (SPI), a standardised temperature index (STI), the Standardised Precipitation–Evapotranspiration Index (SPEI), and SDI derived from observed streamflow. All indices are approximately standardised and show frequent negative excursions, indicating recurrent meteorological and hydrological droughts. Correlation analysis revealed that short-term precipitation anomalies significantly influence the linear control of SDI. Specifically, SPI at a one-month lag exhibits the strongest association (r ≈ 0.50), followed by contemporaneous SPI (r ≈ 0.44) and a two-month lag (r ≈ 0.24). By contrast, STI and SPEI lags exhibit negligible correlations, indicating that temperature-driven evaporative demand plays a secondary role in the initial onset of drought in this temperate, precipitation-dominated basin. We evaluated several machine-learning models for one-month-ahead SDI prediction, including Random Forest (RF), XGBoost, Elastic Net, Support Vector Regression (SVR), and a Multilayer Perceptron. Models were trained on the first 80% of the record and evaluated on the remaining 20% using a strictly chronological split. For RF, key hyperparameters (number of trees, maximum depth, leaf size and feature subsampling) were tuned using Randomized Search with Time Series Split cross‑validation. A linear‑scaling bias correction was applied to align the mean and variance of predicted SDI with observations in the training period. Random Forest clearly outperformed the alternative models. In the independent test period (2009–2020), the bias‑corrected RF achieved MAE ≈ 0.62, RMSE ≈ 0.83, and NSE ≈ 0.49, explaining almost half the variance in SDI. KGE ≈ 0.65 indicates good joint reproduction of correlation, variability and bias. The model accurately captured the timing and sign of most wet and dry episodes, while moderately underestimating the most extreme peaks. Other algorithms exhibited similar or larger errors and substantially lower KGE, confirming RF as the most suitable SDI forecasting approach in this index‑only setting. Finally, we drove the optimised RF with SPI/STI/SPEI projections from RCP2.6, RCP4.5 and RCP8.5 to generate monthly SDI projections for 2021–2050. Hydrostripes and distributions show clear scenario‑dependent changes: RCP2.6 maintains mainly mild, short‑lived droughts; RCP4.5 produces more persistent and clustered deficits; and RCP8.5 yields the most frequent and severe hydrological droughts. The framework demonstrates that a carefully tuned Random Forest, using only standardised meteorological indices, can provide skilful and interpretable SDI projections to support climate‑informed drought risk management.Acknowledgment: This research was supported by the “Streamflow Drought Through Time” project funded by the EU NextGenerationEU through the Recovery and Resilience Plan of the Slovak Republic within the framework of project no. 09I03-03-V04-00186.