Extreme flooding events increasingly threaten urban settlements, making advanced remote sensing approaches essential for effective monitoring and assessment. This study presents a novel methodology that combines multiple spectral indices with K-Means clustering to map flood extent and intensity using Sentinel-2 imagery, focusing on the extreme flooding event that affected Porto Alegre, Brazil, in April-May 2024. The approach integrates the water index MNDWI with complementary indices, including NDVI, NDWI, NIR, and SWIR bands. Additionally, terrain slope and size filtering constraints are applied to minimise false positives while maintaining high detection accuracy. K-Means clustering was employed for automatic threshold determination, creating binary water/non-water classifications across the 1,400 km² study area. The method demonstrated its ability to monitor temporal flood dynamics, revealing a maximum water coverage of 428.2 km² during the peak flooding period in May-June 2024. Statistical analysis indicated consistently negative mean values for MNDWI (-0.44 to -0.46) across all periods. Although the combined approach achieved a high recall rate (84%) for actual water detection, precision was moderate (31-32%) due to spectral confusion arising from wet soil, shadows, and urban features. The enhanced multi-index methodology demonstrated superiority over single-index approaches in complex mixed land-cover environments, offering robust flood intensity mapping capabilities that are crucial for disaster management and risk assessment in vulnerable riverbank communities.