Thailand is the world's largest exporter of cassava. The cassava prices fluctuate because of many factors such as the production cost; economic condition; and price intervention. Therefore; this research aims to propose a forecasting model of cassava price based on the 11-year data (from 2005 to 2015) obtained from the Thai Tapioca Starch Association and Office of Agricultural Economics. Various techniques were applied for the forecast such as Artificial Neural Network; Support Vector Machine; k-Nearest Neighbor and Hybrid Technique. The statistics used to determine the effectiveness of this model were Mean Absolute Percentage Error (MAPE); Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). The results of this research showed that Hybrid Technique demonstrated the lowest value of error followed by Artificial Neural Network; k-Nearest Neighbor and Support Vector Machine; respectively. Therefore; it could be concluded that using the Hybrid Technique to forecast the price of cassava was better than other techniques and generated the predicted price closest to the actual price.