In light of addressing the shortage of agricultural workforce and the consequent rise in the need for integrated machinery, this chapter presents a study that endeavored to construct a remote autonomous system for multi-cluster agricultural machinery by implementing an IoT-based and AI-based system using the WebSocket protocol and the YOLO deep learning algorithm. Considering contemporary machinery’s difficulties, including substantial operational expenses and arduous learning curves, this research aims to enhance operational efficiency for supporting labor shortages and increasing productivity. Utilizing low-cost hardware, the system facilitated coordinated and autonomous operations by enabling real-time communication between machinery clusters and users via cloud services and the WebSocket protocol. Moreover, the system facilitated cost-effective implementation by migrating the YOLO algorithm from an offline environment to the cloud. The system could transmit real-time data, such as state information, commands, and image frames, which were critical for the operation and control of the machine, while also ensuring stability. In addition, orchard experiments demonstrated that the system employed real-time cloud AI for automated irrigation and reduced the cost of AI processing time. Subsequent developments in agricultural production would be facilitated by the system’s establishment of a robust framework for developing autonomy and intelligent machinery.