Abstract Domestic cats ( Felis catus ) have shared a common history with humans since their domestication 10,000 years ago and today they are one of the world’s most widespread predatory mammals. Different populations of domestic cats around the world show a high degree of variability in terms of their autonomy from humans for feeding or moving about, with common descriptions ranging from owned domestic cats to feral domestic cats on the spectrum from the most dependent to the most autonomous about humans. Distinguishing between owned and other domestic cats, I proposed a framework based on machine learning and citizen science data to predict the annual predation pressure on bird species per domestic cat considering traits, phylogeny and geographical distribution. Leveraging the Random Forest model and data from Mori et al. (2019), I assessed the predation pressure for each native continental bird species of the United States. Findings revealed that geographical distribution, phylogeny and traits influenced the predictive value of predation pressure, while a specific trait combination was also associated with high predation pressure. Furthermore, 35% of species experienced high predation pressure from owned domestic cats regardless of the existing threats. The results are consistent with former empirical evidence of predation by domestic cats in the United States and highlight the urgency of understanding the ecological impact of domestic cats. By producing a quantitative value for predation pressure, the framework allows the development of more reliable models of species extinction risk, including for the effects of domestic cat predation, and thus the use of more specific management strategies to sensitive populations. Although the study requires further refinement, the framework offers promising insights. With expanded citizen science protocols, it could help improve the extinction risk models and guide precise management strategies, which are crucial for mitigating the impact of domestic cats on native wildlife.