With the swift expansion of Internet of Things (IoT) networks, it has become crucial to ensure real-time, low-latency communication, especially in vital areas such as autonomous vehicles, industrial automation, and healthcare. Conventional routing protocols, like Ad hoc On-Demand Distance Vector (AODV) and Destination Sequenced Distance Vector (DSDV), often fail to remain efficient in the dynamic and distributed nature of IoT, leading to considerable communication delays. This paper explores AI-enhanced routing strategies that dynamically optimize data paths by evaluating real-time network conditions and learning from past data. The study utilizes essential AI techniques, including machine learning-driven decision-making, Reinforcement Learning (RL) for adaptive route management, and AI-assisted congestion management, to improve real-time routing choices. Our results indicate that these AI-based methods successfully reduce latency and enhance network performance, rendering them suitable for latency-critical IoT applications. Furthermore, AI-enabled routing shows potential for adjusting to network changes and device mobility, thus ensuring sustained low latency. Future studies will aim at expanding these approaches to larger networks and strengthening their security to ultimately meet the increasing requirements of real-time IoT systems.