Globally, Thailand have been one of the top 5 rice exporting countries and Thai rice is also known as fragrant rice. Over the past 5 years, Thailand have exported rice approximately 8.46 million tons per year to major export partners such as China, Japan, the United States, and the European Union. However, the patterns of Thailand’s rice exports quantity have illustrated the variation and instability which cause of inaccurate forecasts. The aim of this study is to propose the forecasting solutions by determining the significant trends and analyzing the affecting factors of Thailand’s rice exports. The proposed model explores 4 forecasting techniques including Backpropagation Neural Network (BPNN), Holt-Winters (HW), Multiple Regression (MR), and Exponential Smoothing (ES). Therefore, the results reveal that Backpropagation Neural Network is the optimal solution and data correlation for training set 0.889, and testing set 0.832 with accuracy 85.187%. The 8 major factors significantly affect Thailand Hommali rice export including Price index of Jasmine rice per field, Thailand policy interest rate, Currency exchange rate, Loan interest rate of Export-Import Bank of Thailand, Gold price, Natural gas price, Food price index, and Producer price index. Ultimately, the results of this study emphasize the importance of demand forecasting to estimate and predict consumers’ future demand with a purpose for making better-informed supply decisions as well as enhancing total system effectiveness of supply chain in the competitive market and unpredictable environment for the future of rice production and consumption.