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The goal of this research is to improve agricultural decision-making by employing machine learning models and ensemble approaches to forecast the production of agricultural goods, particularly Crop, Vegetables, and Fruits (CVF). Several machine learning techniques, including Random Forest, Gradient Boosting, and ensemble approaches, are used to increase forecast accuracy by utilizing historical climate, soil, and yield data. By combining several models, prediction errors are reduced and reliability is increased, giving farmers and other stakeholders important information for maximizing resource allocation, raising productivity, and guaranteeing food security. In order to facilitate data-driven agricultural planning, this research attempts to close the gap between conventional farming methods and sophisticated predictive analytics.