Flood vulnerability assessment is critical for effective disaster risk management in flood-prone regions like Sylhet Division, Bangladesh. This study applied a multi-stage, index-based approach grounded in Principal Component Analysis (PCA) to evaluate social vulnerability across the division at the upazila level. The framework integrated three key dimensions: socio-economic characteristics, infrastructure, and exposure, with indicators selected based on local relevance, data availability, and literature review. Indicators included demographic variables (age, gender, employment), urbanization metrics, population density, and access to safe drinking water, sourced from Bangladesh Bureau of Statistics, satellite remote sensing, and national hazard reports. Data normalization and PCA were applied, with three principal components explaining 76% of the variance: socio-economic (46.7%), infrastructure (17.3%), and exposure (12.1%). Weights derived from factor loadings were used to construct a composite Social Vulnerability Index (SoVI), mapped at the upazila level using ArcGIS. Results reveal significant spatial heterogeneity in vulnerability. Socio-economic factors were the most influential, with 26.83% of upazilas classified as highly vulnerable due to aging populations, unemployment, and gender disparities. Infrastructure vulnerability (7.32% extreme vulnerability) was linked to unplanned urbanization in floodplains, while exposure vulnerability (14.63% extreme) correlated with high population density and limited water access. Composite vulnerability mapping identified hotspots, with over 51% of the region falling into high or moderate vulnerability categories. The study recommends the need for targeted interventions, prioritizing socio-economic resilience alongside infrastructure and exposure mitigation. The upazila-level assessment provides granular insights for policymakers to allocate resources effectively and enhance flood risk management strategies in vulnerable communities.