Surface water systems face unprecedented stress due to climate variability, urbanization, land-use change, and growing water demand—prompting a shift from traditional hydrological modeling to intelligent, adaptive systems. This review critically explores the integration of Artificial Intelligence (AI) in surface flow management, encompassing applications in streamflow forecasting, sediment transport, flood prediction, water quality monitoring, and infrastructure operations such as dam and irrigation control. Drawing from over two decades of interdisciplinary literature, this study synthesizes recent advances in machine learning (ML), deep learning (DL), the Internet of Things (IoT), remote sensing, and hybrid AI–physics models. Unlike earlier reviews focusing on single aspects, this paper presents a systems-level perspective that links AI technologies to their operational, ethical, and governance dimensions. It highlights key AI techniques—including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformer models, and Reinforcement Learning—and discusses their strengths, limitations, and implementation challenges, particularly in data-scarce and climate-uncertain regions. Novel insights are provided on Explainable AI (XAI), algorithmic bias, cybersecurity risks, and institutional readiness, positioning this paper as a roadmap for equitable and resilient AI adoption. By combining methodological analysis, conceptual frameworks, and future directions, this review offers a comprehensive guide for researchers, engineers, and policy-makers navigating the next generation of intelligent surface flow management.

Hydrological forecasting has evolved rapidly in response to intensifying climate variability, increasing data availability, and advances in computational modeling. This review synthesizes developments from 2006 to 2025, examining four major forecasting domains: statistical approaches, physically based models, data-driven machine learning and deep learning techniques, and hybrid or emerging physics–AI frameworks. Recent literature shows a decisive shift toward integrated, data-rich systems that leverage remote sensing, IoT networks, and artificial intelligence to overcome limitations in traditional forecasting. While hybrid and physics-informed AI models achieve notable improvements in accuracy, lead time, and scalability, persistent challenges remain, especially regarding data scarcity, model interpretability, cross-basin generalization, climate non-stationarity, and operational computational demands. This review highlights these limitations and outlines future directions needed to strengthen hydrological forecasting as a tool for climate adaptation, early warning systems, and long-term water resource planning. By consolidating methodological advances and emerging gaps, the study provides insights into how hydrological forecasting can transition toward more resilient, transparent, and decision-oriented frameworks.

Sediment yield remains a major challenge in erosion-prone watersheds because it affects reservoir capacity, water quality, hydraulic infrastructure, and ecological stability. Although numerous studies have examined sediment yield estimation and sediment control, these topics are often treated separately, limiting the development of integrated watershed management strategies. Unlike many existing sediment yield review papers that focus primarily on predictive models, erosion processes, or management measures in isolation, this study provides an integrated synthesis of sediment yield estimation methods and sediment control strategies within a single watershed management framework for erosion-prone environments. The review covers empirical models, traditional sampling, physically based models, and emerging data-driven tools such as artificial intelligence, machine learning, remote sensing, and sensor-based monitoring, alongside structural, vegetative, and adaptive sediment control measures. The reviewed literature indicates three major trends: increasing integration of GIS and remote sensing with conventional models, wider use of process-based models for scenario analysis, and rapid growth of AI-based methods for real-time and nonlinear prediction. The findings further show that no single estimation or control strategy is universally applicable; performance depends strongly on watershed scale, sediment connectivity, land use, climatic regime, and data availability. Overall, the review highlights the need for integrated, adaptive, and site-specific sediment management frameworks that combine predictive modeling, monitoring technologies, and practical control interventions to improve long-term watershed resilience.