There are several mechanisms for monitoring the quality of care in long-term care (LTC), including the use of quality indicators derived from resident assessments and formal inspections. The LTC inspection process is time and resource-intensive, and there may be opportunities to better target inspections. In this study, we aimed to examine whether quality indicators could predict future inspection performance in LTC homes across Ontario, Canada.In total, 594 LTC homes across Ontario.Using a database compiling detailed inspection reports for the period from 2017 to 2018, we classified each home into 1 of 3 categories (in good standing, needing improvement, needing significant improvement). Machine learning techniques were used to examine whether publicly available Resident Assessment Instrument‒Minimum Data Set quality indicators for the period 2016‒2017 could predict facility classification based on inspection results.After running a wide range of models, only a weak relationship was found between quality indicators and future inspection performance. The best-performing model was able to achieve a classification accuracy of 40.1%. Feature analysis was performed on the final model to identify which quality indicators were most indicative of predicted poor performance. Experiencing worsened pain, restraint use, and worsened pressure ulcers were correlated with homes predicted as needing significant improvement. Counterintuitively, improved physical functioning had an inverse relationship with homes predicted as being in good standing.Most quality indicators are poor predictors of inspection performance. Further work is required to explore the limited relationship between these 2 measures of LTC quality, and to identify other quality measures that may be useful as predictors of facilities facing difficulty in meeting quality standards.