Significance Human pressures are causing natural ecosystems to change at an unprecedented rate. Understanding these changes is important (e.g., to inform policy decisions), but we are hampered by the slow, labor-intensive nature of traditional ecological surveys. In this study, we show that automated analysis of the sounds of an ecosystem—its soundscape—enables rapid and scalable ecological monitoring. We used a neural network to calculate fingerprints of soundscapes from a variety of ecosystems. From these acoustic fingerprints we could accurately predict habitat quality and biodiversity across multiple scales and automatically identify anomalous sounds such as gunshots and chainsaws. Crucially, our approach generalized well across ecosystems, offering promise as a backbone technology for global monitoring efforts.