A Machine Learning‐Based Forecasting Approaches and Correlation Analysis to Assess Future Extreme Heat Scenarios in Bangladesh and Its Impact on Public Health

ABSTRACT Bangladesh's geographical location as well as dense population makes it highly vulnerable to rising maximum temperatures and the associated public health impacts driven by climate change. Although machine learning‐based forecasting and correlation analysis are widely applied, most existing models lack regional calibration and consequently fail to capture Bangladesh's distinct climatic variability. In addition, the accuracy of forecasting of maximum temperature cannot be determined since the results are inconsistent. This study addresses these challenges by proposing and evaluating four forecasting models: the Autoregressive Integrated Moving Average (ARIMA) model, the Exponential Smoothing State‐Space (ETS) model, the Holt (Double Exponential Smoothing) model, and the Prophet model. The models forecast maximum temperature and examine its correlation with public health issues—particularly diarrhea cases in Bangladesh—over the period from 2017 to 2040. The study compares the forecasting performance of these state‐of‐the‐art models, showing that maximum temperatures are projected to range between 31.39°C and 34.3°C by 2040. ETS and Holt predicted 3786 and 2282 diarrhea cases respectively at 33.71°C, while Prophet estimated 3841 cases at 34.3°C. ARIMA forecasted 4045 diarrhea cases with a predicted maximum temperature of 32.15°C, achieving a moderate error margin. Among the models, ARIMA demonstrated the most balanced and reliable performance, confirming its effectiveness for climate and health forecasting in the Bangladeshi context. Overall, this work outperforms existing approaches by delivering consistent and accurate temperature forecasts. Additionally, it bridges a significant gap in the literature by establishing a clear correlation between maximum temperature and diarrhea cases in Bangladesh. The findings offer actionable insights for policymakers and public health officials, supporting more effective strategies for public health management and climate adaptation planning.