This document discusses the application of the Extreme Learning Machine (ELM) algorithm for predicting the Effective Drought Index (EDI) in eastern Australia, demonstrating its superior performance over traditional Artificial Neural Networks (ANN) in both prediction accuracy and computational speed. The study utilizes meteorological data and large-scale climate indices to assess drought conditions, finding that the ELM model results in lower error metrics and faster training times compared to ANN. The research highlights the importance of effective drought prediction for water resource management, agriculture, and ecosystems.