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Traditional asset management of lighting systems typically focuseson functionality, cost, and lifespan. In contrast, a human-centricapproach prioritizes social sustainability and user well-being byensuring lighting assets “provide the right light at the right time”for diverse activities. Light-emitting diode (LED) bulbs, known forenergy efficiency and longevity, have become a preferred choice,yet public libraries often struggle to manage these assets sustain-ably, remaining in a reactive “fix/replace when it breaks” stage.Current predictive methods, such as artificial intelligence andmachine learning, rely on laboratory data that often overlook real-world contexts, leading to performance gaps. This paper presents acontext-driven, human-centric methodology for LED prognosis andmaintenance strategies in public libraries, employing limited degra-dation data from LED testing. Advanced analytical techniques,including Markov Chain Monte Carlo (MCMC) and DevianceInformation Criterion (DIC), support a shift from function-based toperformance-based reliability assessment. By incorporating MeanTime of Exposure (MTOE) and Critical Integrated Levels (CILs), theapproach defines optimal maintenance inspection intervals. Thisresearch enhances sustainable LED lighting management in publiclibraries, offering a framework adaptable to broader applicationsand aligned with human-centric goals.