This research investigates the nature relation between global commodity prices and agribusiness stock performance, focusing on CPIN stock, and employs stacking ensemble model that integrates both traditional and advanced machine learning models. With data covering over 19 years, we use statistical tests, correlation analysis, and predictive modeling to bridge the gap about the link between commodities and stock prices. Result indicates significant correlation between CPIN stock and commodities, particularly soybeans (p = 0.0063), highlighting distinct market influences. The ensemble model enhances overall prediction accuracy for commodity prices by leveraging complementary model strengths, though slightly underperforms neural networks in stock price predictions due to model aggregation and a dataset more readily recognized by individual models, particularly neural networks. These insights underscore complex market dynamics and variability across data types, providing valuable perspectives for investors, risk managers, and policymakers in agricultural finance.