<p>New product development (NPD) in the agricultural sector is a risky endeavor that requires effective risk management. However, most existing methods for risk management are based on single-link trees or lists that do not capture the complexity and interdependence of risks. Furthermore, previous research on risk management focused on designing responses for individual risks. However, many businesses face constraints in terms of time, budget, and resources, making it challenging to implement all the designed responses for each risk. Consequently, effective risk management becomes difficult, leading to future challenges. To address this limitation, this study proposes an alternative approach for risk response planning by utilizing a machine learning algorithm to cluster risks based on defined value measures, thereby enhancing knowledge creation and diffusion in the risk management field. It introduces the concept of project value as a criterion for assessing risk importance, in addition to project time and cost. In this paper, we propose a novel framework for risk management in agricultural NPD projects that uses machine learning to cluster risks based on various features. We apply this framework to a case study of an agricultural company that developed a new product. We demonstrate how clustering can help identify and understand similar and dissimilar risks, both positive and negative, and design appropriate risk response strategies. The case study results highlight the need for focused attention on production and marketing risks and emphasize the significance of suitable responses for preventing project failure and substantial losses. Furthermore, it suggests specific response actions for each cluster, such as cultivating customer loyalty, adhering to standard production processes, and providing employee training. Additionally, our approach promotes effective communication and coordination among project stakeholders through conducting risk planning at the cluster level. This strategy fosters a collaborative environment for knowledge application as well as improving managerial efficiency and ultimately contributing to the sustainability and success of agricultural NPD initiatives. By aligning risk management with the principles of the knowledge-based economy, this study provides valuable insights for practitioners and researchers aiming to enhance innovation and competitiveness in the agricultural sector.</p>