Background Currently, incidents, including fires on board of aircrafts during takeoff and landing, are becoming more frequent. To address this issue, we introduce new models of fire propagation dynamics and the evacuation process for aircraft passengers, accounting for their physical interactions, along with an integrated model combining such processes as the spread of fire, smoke, and temperature. Nowadays aviation incidents involving onboard fires occur regularly, often resulting in traumas among passengers, as well as material damage. Purpose The main purpose of this study is to create integrated models that enable analysis of aircraft evacuation under various fire hazard scenarios. Much attention is given to using these models to analyze the process of leaving the aircraft, taking into account various scenarios of the spread of damaging fire factors, which will allow us to develop an optimal sequence of actions for each particular situation. Method It uses mathematical apparatus of the multi-dimensional cellular automata to describe fire spread, dividing the aircraft into cubic cells with 4 states: burning, burned, consisting of combustible, and non-combustible materials. Calculation of the probabilities of combustion is based on the influence of the neighboring cells, while evacuation models incorporate multi-agent approaches considering passengers’ movements, physical contacts, and hazardous factor distributions. The model was created, and graphs were obtained using Python 3.12. Results The results indicate that the integrated model accurately simulates fire dynamics and evacuation interactions, allowing us to analyze different scenarios to make scenario-based predictions of optimal post-accident exit routes. The model was implemented for two scenarios: a fire in the left engine of the Embraer E-190 and Airbus A320-100 aircraft. Conclusions Based on the findings, it can be concluded that this approach facilitates decision support systems for enhancing safety during ground-based aircraft fires, providing the model for analyzing and minimizing risks in sudden emergencies.