Mushroom farming is a very delicate and regulated process that requires careful observation of environmental parameters, including temperature, humidity, carbon dioxide levels, and substrate conditions. Therefore, if the environment is not well monitored and controlled, it leads to inefficiencies and inconsistent production. The purpose of this study is to investigate the challenge of limited mushroom production, which does not meet the demand. The primary reason for this issue is determined to be a lack of training and consequently insufficient knowledge about mushroom farming. This work suggests developing a digital twin with multi-agent learning as a training and guidance tool for mushroom producers. Digital twin incorporates smart farming technologies including Internet of Things (IoT), Artificial Intelligence (AI), analytics and cloud computing. Application of digital twins in mushroom farming integrates a physical farm environment and its virtual replica, which are enabled by data and simulators to provide better monitoring, control, and decision-making as well as real-time prediction, and optimization. Although using a digital twin as a training tool for mushroom farmers is not a novel idea, there are several gaps identified in the literature, including the lack of trained AI models and the non-implementation of real-time IoT sensors. By integrating IoT sensors, cloud computing and multiple AI-driven analytics, the study aims to use a digital twin to serve as a training tool to give farmers more control and insight into the cultivation process, which increases sustainability. Despite the lack of a prototype, this idea offers an appealing foundation for potential future use.