<p>Floods are one of the most dreaded hazards adversely affecting life and property which demands and necessitates singular attention. This paper aims at documenting the trajectory of the recent trends and advancements in monitoring and prediction, including probabilities of associated uncertainties from a ‘flood’ point of view. The focus is to convey a comparative vision of vulnerability and risk assessment bringing a viable grasp in approaching problems associated with floods. Over a timeline spanning 17 years (2007–2024), a systematic review is conducted utilising Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, into two sections: monitoring and forecasting techniques. The former encompasses a range of technologies, from ground-level observations to advanced tools, involving global to community level, in perceiving impacts of flood. Monitoring is enhanced by drone technology, sensor cameras, remote sensing and digital image analysis allowing spatial risk visualisation. The latter pays attention to the utilisation of geospatial guides, GIS tools and machine learning (ML) algorithms in predicting the temporal probabilities of flood. The prediction often leverages computer training ensemble model and neural networks (ANN and CNN). The ensemble ML models are observed having 10–15% higher accuracy compared to traditional models. Moreover, hybrid approaches demonstrate superior real-time forecasting even in data-scarce regions. This review article serves as an efficacious resource for researchers and decision-makers, streamlined to enhance understanding of the entire flood hazard management continuum—from initiation to recovery. By scientific analysis, this work will steer future studies on floods and promote adaptive strategies to mitigate their disastrous effects.</p>