The global economy is significantly impacted by changes in the price of primary commodities. As a result, both the academic and professional sectors have paid attention to price predictions for major commodities. The goal of this study is to build an artificial intelligence-based model for one-day market price predictions for important commodities like copper, crude oil, gas, and silver. The information on commodity trading was gathered between 01/2000 and 10/2019. Different models based on group method of data handling (GMDH), long short-term memory (LSTM), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were developed. Theil's U, RMSE, MAPE, MAE, R, and other performance indices were used to compare the models. The findings demonstrated that, in terms of commodity price prediction, the suggested model based on GMDH technique performs better than alternative approaches. A viable alternative for price prediction is the GMDH-based model. For economists and professionals involved in commodity price forecasting, the GMDH can be a useful tool.