Wireless Sensor Networks (WSNs) encounter substantial obstacles in maintaining energy efficient communication, particularly in dynamic or mobile environments where network topology constantly changes. Traditional routing protocols often struggle to adapt to these variations, resulting in high energy usage and a shortened network lifespan. The proposed system integrates dynamic clustering, AI-powered predictive routing, and energy-aware multi-hop communication to address these challenges and to enhance routing efficiency dynamically. By employing a mobility-aware clustering algorithm, sensor nodes are clustered based on their mobility patterns, ensuring efficient intra-cluster communication. An AI-driven prediction model forecasts node mobility and environmental conditions, enabling proactive adjustments to routing paths. The protocol also incorporates energy-aware routing, where nodes select the next hop based on the remaining energy of their neighbors, extending network lifetime. Additionally, mobile sink nodes are introduced to minimize communication distances and balance energy consumption across the network. The proposed solution is further enhanced by sleep scheduling and adaptive duty cycling, allowing nodes to conserve energy by transitioning between active and sleep states based on their role and energy reserves. Simulation results demonstrate that the system significantly reduces energy consumption, enhances packet delivery, and prolongs network lifetime compared to traditional routing algorithms, making it an ideal solution for highly dynamic WSN environments.