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Stroke, a leading cause of global disability and mortality, exhibits significant spatiotemporal associations with environmental pollutants. Predicting daily stroke admissions becomes increasingly important as the population ages. Current prediction research on stroke-related medical services mainly relies on point prediction, which lacks the ability to quantify uncertainty. In this study, we try to develop parametric probability prediction models of stroke admissions based on machine learning and deep learning algorithms. We collected stroke data and environmental data from February 11, 2019 to May 26, 2023 in Chengdu, and employed prediction models encompass negative binomial regression, natural gradient boosting (NGBoost), long short-term memory networks (LSTM), and transformer. For performance assessment, mean absolute error (MAE) is used to evaluate point prediction accuracy, while continuous ranked probability score (CRPS) is applied to assess the quality of distribution fitting.We find that models with the ability to capture and process time-series information demonstrate greater advantages in probabilistic prediction, and among the four evaluated models, the transformer model proves to be the one that delivering more reliable and precise outcomes in both point prediction of admission counts and distribution fitting performance. This probabilistic forecasting approach provides robust evidence-based decision support for healthcare administrators to optimize resource allocation and staffing arrangements, and ultimately helps elevate the quality of medical care for stroke patients.