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However, most degrable piezoelectric materials still suffer from limited electromechanical performance. Here, we report a high\u2010performance poly(3\u2010hydroxybutyrate):zinc oxide (P3HB:ZnO) composite piezoelectric nanofiber optimized using an integrated artificial neural network\u2010genetic algorithm framework. The model identified an optimal ZnO content of 4.3\u2009wt.%, resulting in significantly enhanced piezoelectric performance compared with pristine P3HB. The optimized nanofibers achieved a peak voltage sensitivity of 0.382\u2009mV\/kPa at 1\u2009Hz and an effective piezoelectric voltage coefficient of 13.65\u2009mV\/m\u00b7N, with stable output over 1000 loading cycles and a response delay of 318\u2009ms. Biodegradation studies showed approximately 38% mass loss after 6 weeks, while the nanofibers retained measurable piezoelectric functionality within this timeframe. Furthermore, integration with a multichannel sensing platform enabled a proof\u2010of\u2010concept AI\u2010assisted gait\u2010recognition demonstration. This work presents a scalable machine\u2010learning\u2010driven strategy for designing high\u2010performance sustainable piezoelectric materials for self\u2010powered transient bioelectronics.<\/jats:p>","DOI":"10.1002\/smsc.70355","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T17:38:21Z","timestamp":1785346701000},"source":"Crossref","is-referenced-by-count":0,"title":["Sustainable P3HB:ZnO Composite Piezoelectric Nanofibers for AI\u2010Driven Gait Monitoring"],"prefix":"10.1002","volume":"6","author":[{"given":"Milad","family":"Razbin","sequence":"first","affiliation":[{"name":"School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Ruan","sequence":"additional","affiliation":[{"name":"School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney  Sydney New South Wales 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