This study presents a near-real-time flood-mapping approach that integrates satellite-based Earth observation (EO) data, digital elevation models (DEMs), and machine-learning (ML) techniques. Several publicly available flood datasets were evaluated; however, none fully met the requirements for spatial coverage, data quality, and thematic diversity needed for robust model development. To address these limitations, a dedicated training dataset was constructed using Copernicus Emergency Management Service (EMS) Rapid Mapping products, comprising 38 flood events from 2022 to 2025. A modular workflow was developed to generate ML-ready datasets from satellite imagery, including data acquisition, advanced preprocessing, flood mask generation, and image tiling. Additional steps, such as co-registration, rescaling, data fusion, and masking irrelevant regions, were implemented to ensure spatial and temporal consistency across heterogeneous inputs. The developed model demonstrates reliable performance in delineating flood extents, achieving an average IoU of 0.70 on the validation dataset. Although the system remains under active development, the results indicate strong potential for operational deployment in near-real-time flood monitoring.