This research aims to study a development of a forecasting model to predict a monthly paddy price in Thailand with 2 datasets. Each of datasets is the univariate time series that is a monthly data, since Jan 1997 to Dec 2017. To generate a forecasting model, we present a forecasting model by using the Artificial Neural Network technique and determine its structure with Autoregressive form of the ARIMA model and Genetic Algorithm, it’s called AR-GA-ANN model. To generate the AR-GA-ANN model, we set 1 to 3 hidden layers for testing, determining the number of input nodes by an Autoregressive form of the ARIMA model and determine the number of neurons in hidden layer by Genetic Algorithm. Finally, we evaluate a performance of our AR-GA-ANN model by error measurement with Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) and compare errors with the ARIMA model. The result found that all of AR-GA-ANN models have lower RMSE and MAPE than the ARIMA model and the AR-GA-ANN with 1 hidden layer has lowest RMSE and MAPE in both datasets.