ABSTRACT Accurate forecasting of agricultural prices is essential for informed production planning, market stabilisation and effective policy design. This review examines 773 studies published between 2006 and 2025 to synthesise recent advances. We begin by analysing the factor systems and structural characteristics of agri‐price data, and organise forecasting tasks by input–output design, temporal resolution and prediction objectives—ranging from point estimates to trend detection and probabilistic forecasting. Evaluation practices are reviewed across multiple dimensions, including error metrics, trend alignment, model selection and uncertainty estimation. We then trace the evolution of forecasting 15 methods from traditional statistical models to machine learning and deep neural 16 architectures (RNN, CNN, GNN, Transformer), as well as decomposition‐based and 17 ensemble strategies. These developments are contextualised within bibliometric trends, highlighting shifts in research focus and global collaboration. Empirical evidence shows that hybrid pipelines combining decomposition, feature learning and ensemble techniques tend to outperform standalone models, while simple linear models remain competitive for long‐horizon or low‐frequency forecasts. Common challenges include data leakage, inconsistent testing horizons and insufficient treatment of uncertainty. Looking ahead, future research should emphasise integrating diverse data sources—such as weather, trade and policy signals—and building models that can adapt to unexpected market changes. It is equally important to understand how price dynamics respond to policy actions, improve model transferability across regions and commodities and provide well‐calibrated forecasts with interpretable uncertainty estimates to enhance the practical value of agricultural price prediction.
Wu et al. (2026) studied this question.