Wildfires are a natural disturbance which can cause harm to people, local ecology, the environment and infrastructure. Wildfires are theorised to have increased both in frequency and scale due to climate change. This has expanded interest in managing and reducing large scale, often catastrophic wildfires. A critical part of achieving this requires real-time fire line prediction. This provides insight as to how resources can be deployed to reduce the propagation of wildfires. Real-time fire line prediction is usually performed using a wildfire model. There are many frameworks wildfire models can simulate fire propagation with. A grid-based framework for modelling wildfires was developed in this thesis. Different types of grids were compared using this framework. A comparison was also made with a continuous-based framework. The use of a grid allowed the wildfire model to run fast. A critical research area regarding wildfire modelling often overlooked is the task of finding where a wildfire started and how long that wildfire burned. This information is important as it may help fire investigators determine the cause of the wildfire, which can be insightful for preventing future wildfires. A genetic algorithm which estimated both the wildfire starting locations and the propagation duration using its burn scar data was developed in this thesis. Typically, wildfire models are derived from experimental data. An investigation into the feasibility of using a desktop apparatus to model wildfires was completed in this thesis. The desktop apparatus was also used to validate the developed grid-based framework. A significant portion of this thesis was completed in collaboration with DecaMap, a group based in Maynooth University with the goal of developing emergency event management systems. This research was also completed with input from the Irish Fire Service and associated services.