This study introduces a methodological framework for facilitating Machine-to-Machine (M2M) communication among autonomous systems originating from diverse domains, such as Nao Robots, Darwin (humanoid robots), and drones. This is achieved through the establishment of machine-to-machine connectivity utilizing the MQTT Mosquitto broker. Acting as a mediator, this broker facilitates the seamless transmission and conversion of various data types between devices and the central host. Consequently, this eliminates the requirement for the participating machines to share a common domain for communication. By centralizing the connection of all autonomous machines to the broker, they can efficiently transmit and relay essential data to the host for processing. Within the host environment, a sophisticated deep learning model is employed to analyze sensor data received from all autonomous machines. Subsequently, based on this analysis, decisions are autonomously made regarding task allocation to the most suitable machine, ensuring the fastest and most efficient execution without human intervention. This research thus serves as a fundamental cornerstone in the realm of M2M communication, facilitating essential decision-making processes through deep learning mechanisms, while minimizing human involvement. For real-time classification, incremental learning has been utilized to continually merge new data with historical data, enabling ongoing evaluation.