This survey paper conducts a thorough examination of the global challenge posed by urban flash floods, analyzing the intricate factors influencing the occurrence and severity of these events. Drawing on a diverse array of literature and case studies, the study delves into the complex dynamics of urban flash floods and their far-reaching impacts on human populations, infrastructure, and the natural environment. Highlighting the imperative for proactive flood risk management and resilient urban planning, the research puts forth actionable recommendations to bolster urban resilience in the face of mounting flood risks. In addition to evaluating machine learning algorithms like Iso clustering and ensemble classifiers for rapid flood mapping and precise flood susceptibility assessment, the study also explores the utilization of satellite imagery for enhanced flood monitoring and analysis. By integrating insights from both machine learning techniques and satellite imaging, the research aims to provide a comprehensive understanding of urban flash floods and offer practical strategies to mitigate their adverse effects. The study underscores the critical importance of implementing robust flood management measures, advanced early warning systems, and sustainable urban development practices to address the escalating threat of flash floods in urban areas and safeguard vulnerable communities, critical infrastructure, and ecological systems from the devastating consequences of these events.