This study developed price forecasting models for the CEPEA/ESALQ White Crystal Sugar and Hydrous Ethanol Fuel indicators, incorporating variables related to sugarcane supply and climatic conditions. Machine learning models such as Long Short-Term Memory (LSTM), Transformer, and Multilayer Perceptron (MLP) were used, along with the statistical Autoregressive Integrated Moving Average (ARIMA) model, all incorporating exogenous variables. Among these variables are estimates from the Modular Agronomic Simulator for Sugarcane (SAMUCA), data from the National Supply Company (Conab), actual production figures, and a climate indicator constructed from temperature, precipitation, and solar radiation data provided by NASA POWER. Results indicate that models using SAMUCA production estimate exhibited accuracy comparable to those based on historical production data (used as a benchmark), with minor variations in error metrics. The MLP model achieved the best performance for sugar (MAPE of 3.84%), while LSTM was most effective for ethanol (MAPE of 1.87%). Machine learning techniques outperformed traditional methods in capturing seasonal, climatic, and nonlinear patterns. The proposed approach enables price forecasting in advance of the harvest and allows for monthly updates, offering a strategic tool for market stakeholders. The model is also adaptable to other crops and regions with limited production data availability.