This research aims to identify the factors that influence the weight of Grey oyster mushroom from smart farm and develop the models to predict the weight of mushroom by using two models namely Regression model and Artificial Neural Network model (ANN). And compare the accuracy of the forecasters the percentage of the mean absolute percentage error (MAPE) and the square root of the mean square error (RMSE) were used. The data used in the study were the weight of mushroom, average temperature, average humidity, and the amount of water used. The analysis was conducted for variables that influence the weight of the mushroom by using correlation analysis techniques and neural network methods. The results obtained from the comparative study reveals that ANN provide higher accuracy than Regress Model as ANN structure is non-linear, and it can capture the pattern of relationship between input variables and the weight of mushroom better than Regression Model.