A data-driven street flood warning system that employs distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) and machine learning (ML) technologies and techniques to provide a prediction of street flood status along a telecommunications fiber optic cable route using the DFOS/DAS data and ML models. Operationally, a DFOS/DAS interrogator collects and transmits vibrational data resulting from rain events while an online web server provides a user interface for end-users. Two machine learning models are built respectively for rain intensity prediction and flood level prediction. The machine learning models serve as predictive models for rain intensity and flood levels based on data provided to them, which includes rain intensity, rain duration, and historical data on flood levels.