In the Internet of Things (IoT) era, the MQTT Protocol played a bigpart in increasing the flow of uninterrupted communication betweenconnected devices. With its functioning being on the publish/subscribe messaging system and having a central broker framework, MQTTconsidering its lightweight functionality, played a very vital role inIoT connectivity. Nonetheless, there are challenges ahead, especiallyin energy consumption, because the majority of IoT devices operateunder constrained power sources. In line with this, our research suggests how the MQTT broker can make an intelligent decision usingan intelligent algorithm. The algorithm idealizes wake-up times forsubscriber clients with the aid of previous data, including machinelearning (ML) regression techniques in the background that producesubstantial energy savings. The study combines the regression machine learning approaches with the quality of service levels’ incorporation into the decision framework through the introduction ofoperational modes designed for effective client management. The research, therefore, aims universally to enhance the efficiency availablein MQTT making it applicable across diverse IoT applications by simultaneously addressing both the broker and the client sides . Theversatile approach ensures more performance and sustainability forMQTT, further strengthening its build as one of the building blocksfor energy efficient and responsive communication in the IoT. Deeplearning approaches that follow regression will be the required leapfor the transformation of energy consumption and adoption of resource allocation within IoT networks to an optimization level thatwould unlock new frontiers of efficiency for a sustainable connectedfuture.