Recent advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have significantly improved predictive modeling and decision-making in agriculture and agroeconomics. This review critically analyzes 17 influential studies from 2021 to 2024 to evaluate the effectiveness of AI/ML approaches, including deep learning models such as Long Short-Term Memory (LSTM) networks and Transformers, hybrid architectures like Fuzzy-Neural Networks, and traditional econometric techniques, for crop price forecasting, yield prediction, and supply chain optimization. Empirical evidence shows that LSTM and Transformer models reduce forecasting errors by 15% to 30% compared to classical models like ARIMA. At the same time, some hybrid approaches achieve R2 scores exceeding 0.90 in highly variable market settings. Nonetheless, persistent challenges such as data sparsity in smallholder contexts and the computational demands of DL architecture can result in accuracy reductions of up to 25% and limited practical deployment. The review highlights emerging trends, including federated learning for secure model training and quantum ML for large-scale optimization. This review brings together recent developments and measurable outcomes in agricultural AI, helping guide future research toward building practical, affordable, and easy-to-use technologies that meet the real needs of farmers and agricultural systems.