<p>Forecasting the producer price index (PPI) for living materials in China provides leading indicators of inflationary pressures and cost dynamics affecting national economic stability and global supply chains. Precise PPI projections supply policymakers, market participants, and enterprises with essential insights for refining monetary policy, pricing strategies, and resource distribution. This research introduces a novel forecasting architecture employing Gaussian process regression (<i>GPR</i>). The model’s hyperparameters are determined through a Bayesian inference procedure, facilitating real-time adaptation to latent market fluctuations and unforeseen structural shifts. Incorporating these dynamic characteristics enables our methodology to more accurately represent alterations in the trajectory of China’s PPI. Empirical evaluation utilizes monthly data spanning October 1996 to February 2025, encompassing multiple phases of regulatory reform, industrial transformation, and macroeconomic evolution. Model validation is conducted over an out-of-sample interval from June 2019 through February 2025, yielding performance metrics comprising a relative root mean square error (<i>RRMSE</i>) of 0.0235%, a root mean square error (<i>RMSE</i>) of 0.0235, a mean absolute error (<i>MAE</i>) of 0.0186, and a correlation coefficient (<i>CC</i>) of 0.99974. To our knowledge, this work presents the inaugural implementation of a Bayesian-inference-parameterized <i>GPR</i> model for predicting China’s living-materials PPI. Beyond contributing to theoretical advancements in machine-learning-driven price forecasting, the proposed approach furnishes a versatile analytical framework suitable for analogous macroeconomic time-series prediction problems.</p>