With the worsening of climate change, the complications brought on by floods every year create an increasing need for forecasting systems that humanitarian organizations can use to help populations in danger. This research presents a literature review of machine-learning models for impact-based flood forecasting, and compares them with existing humanitarian projects. The results examine the characteristics of the models surveyed, while the discussion focuses on understanding how these characteristics can define whether the machine learning models proposed can actually be translated to humanitarian settings. The main takeaways include the prevalent choice of deep learning and ensemble models, used to improve the adaptability of the models, the problems with data availability and data quality in different areas considered, and the difference between lead times, usability, and scalability of the models proposed in contrast with already used humanitarian projects. This study then highlights the importance of transparency and reproducibility of the survey by detailing the queries and databases used, ensuring accessibility of selected articles, and explaining the selection criteria and methodology. Ultimately, the review concludes with the key insights on the connection between academic prototypes and real-life humanitarian projects, as well as key areas for future research.