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While fluorescence lifetime microscopy is a maturing field, achieving effective 3D imaging in deep tissues remains challenging due to high scattering. In this study, we present a deep neural network\u2010based approach, AUTO\u2010FLI, which enables both 3D intensity and quantitative lifetime reconstructions at centimeter depths. Unlike conventional approaches that estimate lifetime from 2D measurements prior to reconstruction, AUTO\u2010FLI directly recovers voxel\u2010wise lifetime within a three\u2010dimensional domain. The proposed method incorporates an in silico framework to generate fluorescence lifetime data for training and validation. The model is further validated using experimental data acquired on an anatomically accurate mouse\u2010mimicking phantom. The results demonstrate accurate 3D estimates of both intensity and lifetime distributions in highly scattering media, supporting fluorescence lifetime\u2010based molecular imaging at mesoscopic and macroscopic scales, with potential applications in pre\u2010clinical research and fluorescence\u2010guided surgery.<\/jats:p>","DOI":"10.1002\/jbio.70362","type":"journal-article","created":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T22:50:42Z","timestamp":1789944642000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Framework for\n                    <scp>3D<\/scp>\n                    Fluorescence Lifetime Estimation"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8266-5254","authenticated-orcid":false,"given":"Navid Ibtehaj","family":"Nizam","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Rensselaer Polytechnic Institute  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