We interact with environments through our senses: taste, sight, smell, touch, and sound. Our human experiences and interactions are extended (within distant worlds) through digital sensing. In turn, our mobile technologies are metaphorical bridges for us to access; wayfinding, personalise experiences, ecologically based IoT and more. Digital sensing enables us to interact with previously inaccessible locations, gather mutually beneficial data and transition to a more ‘digitally sustainable society’. Dis-tributed technologies and off-the-shelf hardware have transformed accessible and eth-ical interaction opportunities. In turn, machine learning opens landscapes for audio sensing, but is currently hard to access for third sectors. This article documents a design-led technological deployment of machine learning audio technologies, aiming to foster ‘digitally sustainable societies’. Authors use ‘re-search in the wild’ methods, open hardware, and avantgarde conservation sites. Au-thors signpost and comprehend; impacts, pitfalls, and benefits of leveraging machine learning in design-led audio environments. Technological proposals were deployed in; internationally renowned rewilding site, The Knepp Estate, in collaboration with Op-po, technology manufacturer. Situations investigated; explorative, serendipitous, and planned audio interactions, supported by reflections and stakeholder critique(s). Re-views included wildlife rangers, yielding good practice insights for; ‘digitally sustain-able societies’. The research objective comprehends the potential: impacts, pitfalls, benefits, and opportunities of leveraging remote audio interactions, using ‘re-search through design’ & ‘deployment’ methods.