BACKGROUND: The existing risk prediction models for COVID-19 associated mortality have not considered the difference in risk factors in patients across an aging population. AIM: To develop age-specific prediction models to forecast the risk of all-cause mortality in patients recovering from COVID-19 infection. DESIGN: Population-based, retrospective cohort study. METHODS: Patients with COVID-19 between 1 April 2020 and 31 July 2022 survived beyond the acute phase of infection were stratified into separate age cohorts (<45, 45-64, ≥65) and followed-up for one year. Backward stepwise logistic regression and four statistical and machine learning algorithms were employed to develop age-specific models on the risk of post-acute mortality following COVID-19 infection, based on a comprehensive set of clinical parameters including demographics, COVID-19 vaccination status, pre-existing comorbidities and laboratory-test findings. RESULTS: Of the 891,246 patients with COVID-19 identified, 13,578 (1.05%) died within one year of the index date. Age, COVID-19 vaccination status and history of acute respiratory syndrome prior infection were identified as predictors in the models for separate age groups. The model for patients aged ≥65 exhibited excellent prediction performance with an AUROC of 0.87 (95% CI: 0.87, 0.88), followed by the model for patients aged 45-64 [AUROC=0.83 (95% CI: 0.81, 0.85)] and those aged <45 [AUROC=0.79 (95% CI: 0.72, 0.86)]. CONCLUSION: The age-specific models reported accurately predicted the risk of post-acute mortality in their corresponding age-group of patients, providing valuable asset in optimising clinical strategies and resource allocation in the management of the global burden of Long COVID.