<p>Mountainous regions are experiencing increasingly frequent and devastating floods under climate change, demanding urgent advancements in numerical modeling for risk mitigation. Through a comprehensive bibliometric analysis, this study maps the evolution of mountain flood models, revealing that 81% of the research aligns with climate action goals. While traditional tools like HEC-RAS and SWAT dominate current applications, machine learning (ML) adoption has surged exponentially since 2020. However, high-fidelity hydrodynamic models are hampered by prohibitive computational costs, while ML techniques struggle with data scarcity and region-specific biases. Challenges in model structure, parameterization, and historical data reliability further undermine predictive accuracy. The study proposes a framework integrating hybrid AI-physics models, emerging computational technologies, and self-adaptive systems. Physics-Informed Neural Networks (PINNs) and transfer learning to overcome sparse data in regions like the Himalayas. ML-based and quantum computing to accelerate ensemble climate-flood simulations and digital twins to minimize uncertainty through real-time data assimilation. General AI assistants that dynamically adjust models based on live sensor networks. The study highlights the need to revise universal hydrological theories for mountainous contexts, where nonlinear processes such as debris flow and rain-on-snow phenomena defy conventional assumptions. By synthesizing bibliometric trends with cutting-edge technical solutions, this study provides a practical roadmap for hydrologists to select, refine, or design models tailored to the unique challenges of alpine flood risk management. The findings advocate a paradigm shift from static models to adaptive, transparent, and computationally scalable systems to safeguard vulnerable communities in a warming world.</p>