This chapter aims to design and evaluate Artificial Neural Networks (ANN), an intelligent data analytic model to predict daily Steadman Heat Index (SHI) using temperature and humidity. Using 15 stations in Australia, trend analysis for the period 1950–2017 is performed using Mann–Kendal test statistics Sen’s slope methods. Twelve ANN models are developed with a three-layer network employing different combinations of the training algorithm, hidden transfer, and output function. The Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms are utilized to determine the best combination of learning algorithms, hidden transfer, and output functions of the optimum ANN model. Assessment of model performance includes the spread and distribution of predicted SHI, Legates and McCabe Index, Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, the Willmott’s Index of Agreement, and Nash–Sutcliffe Coefficient of Efficiency. The designed model appears to be a suitable intelligent data analytic tool for weather prediction, climate change studies, and probable evaluation of dry climatic conditions in the near future replying to historical datasets to model their future values. The findings have implications for disaster risk management particularly mitigating heatwave risk and consequences on human populations, ecosystems, and other areas including agricultural, health, and wellbeing.