Urban flooding has become an increasingly critical challenge for cities worldwide due to climate change, rapid urbanization, and limitations in conventional infrastructure systems. Traditional flood management approaches that depend on centralized gray infrastructure are no longer sufficient to address the growing complexity and uncertainty of urban flood risks. This study examines the integration of smart infrastructure and predictive analytics as an innovative approach to improving urban flood resilience. Smart infrastructure enables real time monitoring and adaptive control of urban water systems through technologies such as sensors and automated networks. Predictive analytics, supported by machine learning and data driven models, enhances the ability to forecast flood events and support proactive decision making. The review highlights how the combination of these approaches improves system responsiveness, situational awareness, and risk management. It also identifies key challenges including data limitations, institutional fragmentation, and governance constraints. The study concludes that integrating technological innovation with effective policy and planning frameworks is essential for developing resilient and adaptive urban flood management systems.