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Abstract Ongoing climate variability and change are increasing the burden of diarrhoeal disease worldwide. Meaningful early warning systems with adequate lead times (weeks to months) are needed to guide public health decision–making and enhance community resilience against health threats posed by climate change. Toward this goal, we trained various machine-learning models to predict diarrhoeal disease rates in Nepal (2002–2014), Taiwan (2008–2019), and Vietnam (2000–2015) using temperature, precipitation, previous disease rates, and El Niño Southern Oscillation phases. We also compared the performance of shallow time-series neural network (NN), Random Forest Regressor, artificial nn, gradient boosting regressor, and long short-term memory–based methods for their effectiveness in predicting diarrhoeal disease burden across multiple countries. We evaluated model performance using a test dataset and assessed the accuracy of predicted diarrhoeal disease incidence rates for the last year of available data in each district. Our results suggest that even in the absence of the most recent disease surveillance data, a likely scenario in most low- and middle-income countries, our NN-based early warning system using historical data performs reasonably well. However, future studies are needed to perform prospective evaluations of such early warning systems in real-world settings.