Infectious disease simulation models are vital tools for predicting the spread of diseases and guiding public health interventions, especially in emergencies such as armed conflicts and natural disasters. This study aims to assess the risks associated with using infectious disease models in these scenarios and provide strategies for mitigating these risks to improve model reliability and utility. The research employs a structured risk assessment framework, integrating ISO 31000 and Failure Mode and Effect Analysis (FMEA), to systematically evaluate technical, operational, ethical, and external risks. The findings reveal several high-priority risks, including data quality issues, political instability, public mistrust, and the rapidly changing dynamics of infectious disease outbreaks. Recommendations are provided to address these challenges, emphasizing the importance of adaptive modeling, community engagement, workforce capacity building, and stakeholder collaboration. The study highlights the need for improved data infrastructure, adaptive modelling techniques, and transparent communication to enhance model effectiveness. The conclusions underscore the relevance of comprehensive risk mitigation strategies in enhancing the applicability of infectious disease models during emergencies. By addressing the identified risks, public health authorities can improve their capacity to respond effectively to infectious disease outbreaks, ultimately protecting vulnerable populations and improving health outcomes in times of crisis.