Artificial intelligence is rapidly transforming our ability to predict hydrologic processes across scales. This session showcases recent advances in AI-driven approaches for hydrologic modeling, forecasting, and risk assessment, spanning methodological innovations and real-world applications. We welcome submissions on topics including, but not limited to: (1) Deep learning models (e.g., LSTM, transformers, diffusion models) for hydrologic prediction; (2) AI-based hydrologic forecasting and early warning systems; (3) Hybrid models integrating hydrologic process knowledge with machine learning for improved hydrologic simulation; (4) AI approaches for hydrologic extreme analysis, risk mapping, and impact assessment; (5) Generative AI and large language models for hydrologic applications; (6) Transfer learning and regionalization for ungauged or data-scarce basins; (7) Uncertainty quantification and interpretability; (8) Integration of multi-source datasets (remote sensing, reanalysis, and in situ) with AI for enhanced hydrologic prediction.