BACKGROUND: Diarrheal infections continue to be a major public health concern in Bangladesh, especially in urban areas where population density and environmental variables increase dissemination risks. Identifying the intricate connections between weather variables and diarrhea epidemics is critical for developing effective public health remedies. METHODS: We deploy the novel approach of Wavelet-Autoregressive Integrated Moving Average with Exogenous Variable (WARIMAX) and the traditional Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) technique to forecast the incidence of diarrhea by analyzing the influence of climate factors. RESULTS: Higher temperatures are associated with greater diarrheal occurrences, demonstrating the vulnerability of diarrheal epidemics to weather fluctuations. The Wavelet-ARIMAX method, which uses wavelet analysis within the ARIMAX structure, is better at forecasting performance and model fit than the standard ARIMAX model. Based on climatic variables, Wavelet-ARIMAX can accurately predict diarrheal occurrence, as indicated by the mean absolute error (MAE), root mean squared error (RMSE), and root mean squared logarithmic error (RMSLE). The outcomes highlight the necessity of employing advanced time-series modeling tools such as Wavelet-ARIMAX to better understand and anticipate climate-health interactions. Wavelet-ARIMAX uses wavelet analysis to identify time-varying patterns in climate-disease interactions, providing useful insights for public health initiatives. CONCLUSIONS: The results of this research have implications for climate-adaptive health planning, emphasizing the need for focused actions to reduce the impact of climate change on diarrheal illness burdens in towns and cities.