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This study reconstructs historical streamflow in the Sava River Basin (SRB), focusing on hydrological variability over extended timescales. Using a combination of Machine Learning (ML) and Deep Learning (DL) models, streamflow patterns were reconstructed from self-calibrated Palmer Drought Severity Index (scPDSI) proxies. The analysis included nine ML models and two DL architectures, with a post-prediction bias correction applied uniformly using the RQUANT method. Results indicate that ensemble methods, such as Random Forest and Gradient Boosted Tree, along with a six-layer DL model, effectively captured streamflow dynamics. Bias correction improved predictive consistency, particularly for models exhibiting greater initial variability, aligning predictions more closely with observed data. The findings reveal that the 2000–2022 period ranks as the lowest 23-year flow interval in the observed record and one of the driest over the past ~500 years, offering historical context for prolonged low-flow events in the region. This study demonstrates the value of integrating advanced computational methods with bias correction techniques to extend hydrological records and enhance the reliability of reconstructions. By addressing data limitations, this approach provides a foundation for supporting evidence-based water resource management in Southeastern Europe under changing climatic conditions. [ABSTRACT FROM AUTHOR]