As Internet of Things (IoT) networks grow quickly, it gets harder to look at data and make networks work better. We study how discrete mathematics structures, like graph theory, partly ordered sets (posets), and lattices, can be used together to model how IoT networks are set up in this paper. This study uses two advanced AI methodologies, Graph Neural Networks (GNNs) and reinforcement learning, to come up with new ways to look at and make judgements about complicated network behaviours. By combining discrete structures with AI-driven methodologies, this strategy makes it easier to recognize patterns, find problems, and share resources in IoT environments.