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.

This review surveys the current state of data used in the development of Machine Learning models for disease outbreak forecasting, with a focus on identifying systemic shortcomings and areas for improvement. A set of 26 development papers was selected and analyzed based on the dataset's attributes such as scope, type, accessibility, and quality. Through a thematic analysis technique, five dominant categories of data failure were identified: structural, procedural, accessibility, logistical and temporary. Hospital-collected data remains the dominant source but is hindered by under-sampling and latency, while non-traditional data sources offer improved responsiveness at the cost of increased pre-processing complexity. Supplementary datasets, such as climate or mobility data, were found to be underutilized, despite their potential to improve forecasting accuracy. Key areas for improvement include the standardization and public availability of datasets, integration of complementary data sources, and use of language models to manage linguistically ambiguous data. The findings suggest that the current data limitations are structural and widespread, requiring procedural and institutional reforms to improve model generalizability and reliability in disease outbreak forecasting.

Natural disasters frequently cause casualties and property losses. Predicting and mitigating the impact of such threats is crucial to the work of humanitarian organizations. The interactions between hazards are best represented through a multi-hazard approach, and machine learning models are well suited for natural hazard prediction. This study presents a systematized literature survey of machine learning in multi-hazard disaster forecasting in the years 2019-2025, focusing on the used models and performance metrics, their applications and feasibility of use, as well as potential cross-applications. There is a wide variety of models and metrics used. The most commonly used models are random forest and support vector machine and the most prevalent performance metric is the ROC-AUC score. The machine learning models generally perform well, with AUC scores above 0.8, though patterns in performance are difficult to examine. Feasibility is defined here as readiness to be used in practice, and the models are rated in the factors that define it. Most of the articles are feasible. Consideration of cross-application is rare and should be extended. This research summarizes the main trends in the field of disaster forecasting, providing a clear reference point for other academics.

Displacement is a focal point of humanitarian aid efforts, since it affects millions of people globally. Mitigating the consequences of forced migration is important for reducing suffering and one way of doing so is through predicting displacement to prioritise resources in advance. To achieve this, machine learning can be used for its ability to analyse larger amounts of data and identify latent structures more efficiently than human experts. Through a systematized literature review, this research evaluates thoroughly six machine learning tools: UNHCR's Jetson, DRC's Foresight and AHEAD, the EU's EUMigraTool and EPS-Forecasting, and the agent-based simulation Flee, in order to assess their suitability to that end. The analysis compares these tools across several criteria, including the way they use data, algorithmic characteristics, and operational use cases. Finally, it makes recommendations about what should be considered and how to choose amongst the tools for displacement prediction.

As humanitarian needs increase while donor budgets decrease, anticipatory strategies are essential for effective crisis response. In this context, machine learning (ML) has emerged as a promising tool for crisis forecasting, offering the potential to support timely interventions and humanitarian decision-making. However, despite rapid developments in ML-based prediction models, questions remain about their practical utility and trustworthiness in real-world humanitarian settings. This study presents a systematic scoping review of 32 academic and gray literature sources to assess the reliability and feasibility of ML systems for conflict forecasting. By analyzing these systems across dimensions such as forecasting scope, data sources, modeling approaches, validation practices, and ethical considerations, the study finds that while some models demonstrate strong predictive performance and methodological rigor, many lack transparent validation, robust error analysis, and operational applicability. The review concludes that while ML systems hold substantial potential for enhancing conflict anticipation, their current real-world readiness is uneven and context-dependent.