*Result*: A multidimensional approach to manage rip current danger and enhancing safety at beaches.

*Rip currents pose a significant threat to beach safety, causing numerous drownings annually. This study integrates video monitoring (Video Beach Monitoring System – VBMS and Smartphone Beach Monitoring System – SBMS), satellite imagery, NavIC-based drifters, XBeach modelling, and YOLO-V5 Artificial Intelligence technology to enhance rip current detection and management at two rip-prone beaches: Rushikonda and RK Beaches, Visakhapatnam on east coast of India. We mapped 231 rip channels using high-resolution satellite data (2015–2017) and identified seasonal patterns via video analysis (2022–2023). Drifters validated rip velocities, while XBeach simulations (with RMSE 0.12 m/s) replicated dynamics. The AI model achieved >77% detection accuracy, reducing misses by combining video and satellite data. The Safe Beach portal delivers daily rip current forecasts for 175 Indian beaches using a statistical model, improving public safety. This approach, tested on Goa beaches (with ~70% AI accuracy), offers a scalable framework for global replication, enhancing tourism and reducing drowning risks; however, challenges remain in resource-limited regions. Research highlights: Integrated video monitoring, satellite imagery, and AI technology effectively identified persistent rip channels at Rushikonda and RK Beaches, enhancing coastal hazard detection. XBeach modelling, combined with NavIC drifter data, provides a robust framework for understanding rip current dynamics. Satellite-derived bathymetry offers a cost-effective method for mapping nearshore topography, improving rip current forecasting. AI-driven detection enables reliable global rip current identification, supporting real-time beach safety alerts. The traffic-light warning system at Rushikonda enhances lifeguard efficiency, promoting safer beaches and sustainable tourism. [ABSTRACT FROM AUTHOR]*