<p>Agriculture commodity pricing prediction has long been important to investors and regulators. This study looks at the Chinese wholesale white wheat market’s weekly price forecast problem for the period of January 1, 2010, to January 3, 2020. Price forecasting for this important commodity price metric has not gotten enough attention in the literature. Our research is aided by Gaussian process regressions, and cross-validation and Bayesian optimizations are used during model training. Between January 5, 2018 and January 3, 2020, the price index was successfully predicted by the constructed models, with an out-of-sample relative root mean square error of 0.0684%. The generated models may be utilized by investors and policymakers throughout their policy analysis and decision-making procedures. The forecasting findings might be helpful in creating comparable commodity price indices as they offer reference data on the price trends that the models predict.</p>