It aims to bring together interdisciplinary contributions that demonstrate how AI techniques—such as machine learning, deep learning, and reinforcement learning—can enhance energy efficiency across various layers of modern communication networks, including 5G, IoT . It aims to bring together interdisciplinary contributions that demonstrate how AI techniques—such as machine learning, deep learning, and reinforcement learning—can enhance energy efficiency across various layers of modern communication networks, including 5G, IoT . The need for energy-efficient wireless communication systems has become more pressing than ever, not only to support greener and more sustainable networks but also to ensure longer device lifespans, especially in battery-constrained applications such as IoT and sensor networks. As 5G becomes more. This paper investigates the energy efficiency optimization for movable antenna (MA) systems by considering the time delay and energy consumption introduced by MA movement. Techniques like machine learning and reinforcement learning can analyze real-time data to predict traffic, optimize routing, manage power levels, and enhance overall network efficiency.