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Recent advances in Artificial Intelligence (AI) and computer vision have opened new opportunities for automated monitoring and analysis in geomorphology. Among these, optical flow methods represent a powerful approach for quantifying surface velocity fields in rivers using video data. This work presents the development and application of a low-cost optical flow tool designed to estimate river surface velocity from fixed monitoring points, offering a practical and scalable solution for hydrological and flood-risk management applications.The proposed method relies on the implementation of an AI-assisted optical flow algorithm capable of tracking the motion of water surface patterns (e.g., ripples, foam, floating debris) in standard RGB video sequences. By leveraging dense flow estimation and adaptive filtering, the tool produces high-resolution velocity maps that can be continuously updated in near real time. The system has been tested in different riverine environments, showing robust performance under varying lighting and flow conditions, and demonstrating its ability to capture both steady and transient flow dynamics.One of the key strengths of this approach lies in its low operational cost and flexibility. The method can be implemented using conventional cameras and open-source software, eliminating the need for expensive. This makes it particularly suitable for establishing permanent observation points in critical areas, such as flood-prone zones or regions with limited monitoring infrastructure. Continuous optical monitoring of surface velocity provides valuable information for calibrating hydrodynamic models, identifying changes in river morphology, and supporting early-warning systems for extreme hydrological events.Beyond the technical development, this research emphasizes the importance of integrating AI-based monitoring tools within broader frameworks for territorial management and risk mitigation. Establishing collaborations with stakeholders such as Basin Authorities, local governments, and civil protection agencies—can significantly enhance the effectiveness of these systems. Shared data platforms and automated AI-driven analytics could enable more proactive responses to extreme events, improving preparedness and resilience in flood-prone communities.In future developments, the integration of deep learning models for feature detection and noise reduction could further enhance the accuracy and robustness of surface velocity estimation. Combining optical flow data with other remote sensing sources (e.g., UAV imagery, satellite observations) could also provide a multi-scale understanding of fluvial dynamics. This research thus contributes to the growing field of AI applications in geomorphology, highlighting how intelligent, low-cost monitoring systems can play a crucial role in sustainable river management and flood risk assessment.