Natural rubber is a key agricultural commodity in Thailand, with export values subject to significant volatility driven by global prices, exchange rates, crude oil prices, and macroeconomic conditions. Despite extensive studies on commodity forecasting, prior research often relies on traditional econometric models or single machine learning techniques, with limited integration of multi-factor economic variables and ensemble learning approaches for improving predictive robustness. This study aims to develop and compare machine learning models for forecasting Thailand’s natural rubber export value using monthly secondary data from 2012 to 2022 (132 observations, 14 variables). The analysis follows the CRISP-DM framework and employs four techniques: k-Nearest Neighbors (k-NN), Random Forest (RF), Neural Network (NN), and a Voting Ensemble model. The dataset is split into 70% for training and 30% for testing, and model performance is evaluated using MSE, RMSE, MAE, MAPE, and R². The results show that the Voting Ensemble model outperforms other models, achieving the lowest prediction errors (MSE = 3,723,671.862; RMSE = 1,929.6818; MAE = 1,502.4775; MAPE = 0.1083) and the highest R² (0.8259). This study contributes by demonstrating the effectiveness of ensemble learning in integrating heterogeneous economic indicators to enhance forecasting accuracy. The findings provide a robust data-driven framework to support strategic decision-making in export planning, production management, and policy formulation for Thailand’s natural rubber industry.