Urban flooding is a continuous challenge as it interferes with normal lives, destroys structures, and endangers the lives of people in urban settlements. In such incidences, people contribute actively to the use of social media to air their feelings and concerns. Nevertheless, the unstructured data of the multilingual data is still underutilized to a great deal. This paper suggests an NLP-based sentiment analysis system to determine the perspectives of the population towards the urban flood relief efforts. The suggested method is based on machine learning and linguistic preprocessing in order to identify crucial problems, noteworthy understanding, and give sentiment designs. The findings reveal how emotion-based analytics are able to enhance the situational cognition of the populace, rationality of assets portion, and government reaction. As evidenced in this paper, NLP methodologies would strengthen citizen-focused and resilient methodologies to achieving effective urban flood control.