This study investigates the performance of various corn seed varieties in organic and conventional farming systems. It compares key parameters such as yield, plant height, cob size, and other relevant traits across both systems. Advanced data mining techniques, including association rule mining and classification algorithms like Decision Trees and Support Vector Machines, will be utilized to identify critical factors affecting corn performance. The study aims to develop predictive models based on these factors to enhance understanding and decisionmaking in agriculture. The findings will offer valuable insights into the comparative benefits of organic versus conventional farming for corn production. Additionally, the research will guide farmers and stakeholders in selecting suitable seed varieties for specific farming methods. By identifying the strengths and weaknesses of each system, the study contributes to the broader goal of promoting sustainable and efficient agricultural practices. The results will be especially useful in addressing the growing demand for sustainable food production while maintaining high yields. Overall, this research aims to bridge gaps in knowledge and support the development of farming strategies that balance productivity with environmental stewardship.