In this chapter, we explore the application of machine learning (ML) and deep learning (DL) techniques to forecast commodity price volatility, emphasizing the integration of climatic data and financial variables. We use an XAI method, namely the Shapley interpretation method, to explain the impact of different variables on the agricultural price risk. As a preliminary consideration, agricultural businesses are supposed to be significantly influenced by environmental factors, particularly climatic anomalies such as El Niño and La Niña. Therefore, understanding their impact is crucial for effective market prediction and risk management. We discuss various predictive models, including time series analysis, machine learning models, and recurrent neural networks (RNNs) , highlighting their ability to handle large datasets and complex patterns. This chapter provides a comprehensive overview of how advanced computational methods can enhance the accuracy of volatility forecasts, to show the substantial benefits for farmers, investors, and policymakers. By integrating diverse data sources, including historical price data and environmental indicators, while illustrating the potential of ML and DL to study commodity trading and financial planning, we observe that climate features do not persistently rank among the top predictors of agricultural price risk in the US market. This might look surprising at first, as the common belief is the great influence of climate on any aspect of agriculture. This can be interpreted as a sign of adequately manageable risk in commodity market prices against natural phenomena.