This research utilized three types of artificial neural network (ANN) methodologies, namely Backpropagation Neural Network (BPNN) with varied training, transfer, divide, and learning functions; Radial Basis Function Neural Network (RBFNN); and General Regression Neural Network (GRNN), to forecast the severity of stem rust. It considered parameters such as mean maximum temperature, mean minimum temperature, mean rainfall, mean average temperature, mean relative humidity, and different wheat varieties. The statistical analysis revealed that GRNN demonstrated effective predictive capability and required less training time compared to the other models. Additionally, the results indicated that total seasonal rainfall positively influenced the development of wheat stem rust. Keywords: Wheat stem rust, Back propagation neural network, Radial Basis Function Neural Network, General Regression Neural Network. 1. Introduction Wheat is among the major cereal crops cultivated in Ethiopia. Ethiopia is the second largest producer of wheat in sub-Saharan Africa [1]. The crop has considered as the main staple food of Ethiopian population particularly in highlands of the country [2],where it has produced in a large volume and 95% of the total production is produced by small scale farmers.