Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3531 2018 2018 College Of Education For Human Sciences, University of Kerbala, Kerbala, Iraq Zainab Zainab College Of Education For Human Sciences, University of Kerbala, Kerbala, Iraq Israa Abdulkadhim Jabbar Al Ali College of Science, University of Kerbala, Kerbala, Iraq Basma Mustafa M.. H. College Of Education For Human Sciences, University of Kerbala, Kerbala, Iraq Ghada Kamil Mustafa Department of Information And Communication Engineering Al-Nahrain Universit, Baghdad, Iraq; Commission of Rightness for Persons with Disabilities and Special Needs, Ministry of Labor and Social Affairs, Baghdad, Iraq Refed Adnan Jaleel Given that plant disease is the primary factor contributing to damage in most plants, decision makers in the agriculture industry are highly interested in enhancing prediction strategies to detect illness in plants at an early stage. This is crucial for ensuring timely and effective plant care. Classifying healthy soybean plants is a dependable and efficient use of noninvasive techniques like machine learning (ML). In this work, we used ML to enhance a smart forecasting model for the prediction of soybean diseases. We utilized two feature selection techniques, namely gain ratio and correlation, two supervised ML algorithms (support vector machine and Random forest) and the cross-validation technique was used for assessing the proposed system, such as accuracy, F-measure, specificity, executing time, and sensitivity. The suggested technique can readily differentiate between soybean plants that are infected and those that are healthy. The suggested approach has undergone testing using a comprehensive collection of soybean characteristics, as well as a subset of attributes. The findings show that performance metrics are impacted when soybean traits are reduced. 2025 2025 55 65 10.54216/FPA.180205 https://www.americaspg.com/articleinfo/3/show/3531