Heuristic algorithm-based RPL (Routing Protocol for Low-Power and Lossy Networks) routing optimization is a specialized research area in IoT networking focused on improving the efficiency, reliability, and scalability of routing in resource-constrained networks. Research papers in this domain explore the use of heuristic and metaheuristic algorithms—such as genetic algorithms, particle swarm optimization, ant colony optimization, and fuzzy logic—to optimize routing metrics like energy consumption, end-to-end delay, packet delivery ratio, and network lifetime. Key contributions include adaptive parent selection, dynamic route maintenance, load balancing, and energy-aware path optimization to enhance network performance under varying topologies and traffic patterns. Recent studies also address challenges such as network heterogeneity, mobility, scalability, and security in RPL-based IoT deployments. By applying heuristic approaches, research in this area aims to enable intelligent, energy-efficient, and high-performance routing solutions for large-scale low-power and lossy IoT networks.