Abstract: Based on the serious drought situation in the Beijing- Tianjin- Hebei region, to find a standard model for drought forecasting in the region, we adopted the principle of machine learning model, based on the relative humidity index (MI) and extreme learning machine model (ELM), and three optimization algorithms including sparrow algorithm (SSA), particle swarm algorithm (PSO) and genetic algorithm (GA). Three optimization models (SSA-ELM, PSO-ELM and GA-ELM) were constructed. The calculation results were compared with ELM model, generalized regression neural network model (GRNN) and BP neural network model. The results showed that: the degree of drought in the BeijingTianjin- Hebei region was generally serious, especially in spring and winter, and the whole region was basically dominated by extreme drought. The GPI of the SSA-ELM was 1.36, ranking 1st among all the models. The SSA-ELM model can be used as a recommended model for drought forecasting in the Beijing-Tianjin-Hebei region.