One of the most significant meteorological risks to South Asia’s infrastructure, livelihoods, and prosperity is flooding. The lack of ground truth and the significant temporal variability of flood risk in data-scarce regions make credible flood risk prediction difficult, as does the need for an interpretable decision-support system. This research presents a mixed artificial intelligence system for monthly flood risk prediction under climatic fluctuations, prioritizing decision relevance over predictive accuracy. A Tree-Based Interpretable Ensemble of Flood Risk Management (IBE-FRM) with XGBoost, LightGBM, and Random Forests, and a Hybrid Temporal Ensemble Decision Support Model (HTEDSM) with LSTM-based sequence learning were created from long-term monthly rainfall and temperature records of Rajshahi, Bangladesh. Applying data-efficient synthetic labeling with percentile thresholds of cumulative rainfall permitted supervised learning and was not confined to whole flood records to estimate flood-prone months. Cumulative rainfall is the most critical driver of monthly flood danger, and lagged and rolling rainfall features provide the best forecasts. The conservative and dependable IBE-FRM model delivers unambiguous and policy-relevant flood risk estimates for daily monitoring and stakeholder communication. Conversely, HTEDSM is more sensitive in time; it records delayed climatic influences that are significant for early warning and preparedness for emergent flood dangers. The discrimination metrics and temporal risk change comparison show that the models have an AUC-ROC of 0.68 and 0.81, respectively, indicating the complexity of climate-driven flood forecasting and the effectiveness of time-based modeling. Overall, the suggested hybrid model demonstrates that combining interpretable ensemble learning with temporal deep learning can provide a balance of robustness, explainability, and the ability to offer early warning. The conclusions suggest that hybrid artificial intelligence systems can enable risk-informed flood control decisions in climatic uncertainty, especially in data-scarce contexts.