Objective Patients, families and community members would like emergency department wait time visibility . This would improve patient journeys through emergency medicine . The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments . Methods Twelve emergency departments provided three years of retrospective administrative data from Australia (2017-19). Descriptive and exploratory analyses were undertaken on the datasets . Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated . Model performance was tested on COVID-19 period data (January to June 2020). Results There were 1,930,609 patient episodes analysed and median site wait times varied from 24 to 54 minutes . Individual site model prediction median absolute errors varied from +/-22.6 minutes (95% CI 22.4,22.9) to +/- 44.0 minutes (95% CI 43.4,44.4). Global model prediction median absolute errors varied from +/-33.9 minutes (95% CI 33.4 , 34.0) to +/-43.8 minutes (95% CI 43.7 , 43.9). Random forest and linear regression models performed the best, rolling average models under-estimated wait times . Important variables were triage category, last-k patient average wait time, and arrival time . Wait time prediction models are not transferable across hospitals . Models performed well during the COVID-19 lockdown period . Conclusions Electronic emergency demographic and flow information can be used to approximate emergency patient wait times . A general model is less accurate if applied without site specific factors.