Globally, flooding has become an inevitable occurrence, increasing rapidly in different parts of the world, including Nigeria. It is a common natural disaster in Nigeria that destroys quantum of lives and infrastructures. This study is basically on flood vulnerability mapping of Ogbaru Local Government Area (LGA), Anambra State, Nigeria, to determine highly vulnerable, moderately vulnerable, vulnerable and less vulnerable areas to flood. SRTM DEM 30m was used to generate the flood contributory parameters, namely, Slope, Distance to River, Flow Direction, Flow Accumulation, Basin, Watershed, and Drainage Density. A Sentinel 2 image of 10m spatial resolution was used to derive the Land Use/Land Cover of the study area using the maximum likelihood algorithm through supervised image classification. Other parameters, such as soil and rainfall, were also derived. The Multi-Criteria Analysis (MCA) method, namely the Analytic Hierarchy Process (AHP), was employed, where ten criteria, including Slope, Flow Direction, Flow Accumulation, Basin, Watershed, Drainage Density, Distance to River, Land use/cover, Rainfall and Soil, were assigned weights according to their order of importance from the most to least desirable criteria. Subsequently, the criteria were reclassified into five classes with the reclassify algorithm using ArcMap 10.8.2 software. A Weighted Overlay model was used to generate a flood vulnerability map of the Ogbaru Local Government Area. The Multi Criteria Analysis revealed that Rainfall, with the percentage of 28% contributed more to flooding than other factors considered in the model, followed by distance to river (20%), slope (15%), soil (9%) and basin (6%). The percentage of areas vulnerable to flood shows Highly Vulnerable (2%), Vulnerable (39%), Moderately Vulnerable (55%), and Less Vulnerable (4%). Accuracy assessment of the supervised image classification was performed using the confusion matrix algorithm. The flood inventory maps from 2018, 2020, and 2022, provided by the Nigeria Hydrological Services Agency (NIHSA 2018; 2020), were used to validate the accuracy of the flood vulnerability map. The high accuracy of the AHP model and weighted overlay model serves as a viable approach for predicting and mitigating floods. This study called for strategic prediction, monitoring, mitigation and prevention of flood.