Data mining is revolutionizing the agriculture sector by enabling farmers, policymakers, and agribusinesses to make informed decisions based on data-driven insights. This abstract provides an overview of the diverse applications of data mining in agriculture, including crop yield prediction, disease detection, market analysis, precision agriculture, livestock management, weather forecasting, soil health assessment, and supply chain management. The abstract highlights the significance of data mining in enhancing agricultural productivity, sustainability, and profitability. By leveraging advanced data analytics techniques, such as machine learning, statistical analysis, and spatial modeling, stakeholders can optimize resource allocation, mitigate risks, and address complex challenges facing the agriculture industry. Through a review of literature and case studies, this abstract showcases how data mining is transforming farming practices worldwide. It emphasizes the need for continued research, innovation, and collaboration to harness the full potential of data mining in agriculture and ensure food security for future generations.