In riverine flood-prone areas, the delineation of the spatial pattern of flood extents and durations allow flood planners to anticipate likely threats from floods and to formulate actions to mitigate these events. Rapid flood mapping is crucial for flood disaster estimation and evaluation in the early stage. Accurate and timely updates of flood inundation have been made possible by remote sensing techniques. The present study applies the Water indexes and Classification method to analyzes and estimates the riverine Spatio-temporal flood-2014 extent changes using Landsat-8 imagery in Lower Chenab Plain, Pakistan. The lower Chenab plain is particularly prone to frequent riverine flooding but is understudied. It has experienced history worst flooding in September 2014. Optical Landsat-8 data can be used for flood inundation mapping when the flooded areas are clouds free. Cloud free Landsat-8 data was acquired for pre-flood, during-flood, and post-flood periods for detailed analysis. We used different water indexes including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) for the delineation of inundated areas based on increase water index value from pre-flood and post-flood Landsat-8 images. Satellite-derived Water Indexes which are mostly utilized for flood extent estimation that separates the flooded water area from non-flooded areas based on a threshold value. Further, we also used supervised classification to detect flooded areas and compare them with water indexes. The resulted analysis allowed us to compute flood extent area, duration, and flood recession. The inundated area values of used water indexes are higher in the post-flood instance as compared to the pre-flood instance. The proposed RS technique provides an empirical basis for the rapid identification of inundated areas, which would enable emergency response and relief efforts on newly flooded areas in future events. Thus, our study provides another perspective and substantial contributions to flood monitoring using free satellite data in Pakistan.