Resumen: Background: Wildfire frequency, magnitude and impacts in wildland–urban interface (WUI) areas are increasing in the Mediterranean Basin.Aims: We investigated the role played by socio-economic, vegetation, climatic, and zootechnical drivers on WUI wildfire patterns (area burned and wildfire ignitions) in Sardinia, Italy. Methods: We defined WUI as the 100-m buffer area of the anthropic layers. We created a comprehensive and multi-year dataset of explanatory variables and wildfires, and then trained a set of models and evaluated their performances in predicting WUI fires. We used the best models to assess the single variable’s importance and map wildfire patterns. Key results: Random Forest and Support Vector Machine were the best performing models. In broad terms, wildfire patterns at WUI were influenced by socio-economic factors and herbaceous vegetation types. Conclusions: Machine learning models can be useful tools to predict wildfire ignitions and area burned at WUI in Mediterranean areas. Implications: Improved knowledge of the main drivers of wildfires at WUI in fire-prone Mediterranean areas can foster the development or optimisation of wildfire risk reduction and prevention strategies. Idioma: Inglés DOI: 10.1071/WF24109 Año: 2024 Publicado en: International Journal of Wildland Fire 33, 12 (2024), [16 pp.] ISSN: 1049-8001 Factor impacto JCR: 2.9 (2024) Categ. JCR: FORESTRY rank: 17 / 92 = 0.185 (2024) - Q1 - T1 Factor impacto SCIMAGO: 0.789 - Forestry (Q1) - Ecology (Q1)