The Internet of Things (IoT) refers to a network of interconnected devices that operate on the internet facilitating seamless and efficient data exchange to improve human life. Energy consumption in the IoT network nodes is a major challenge. To overcome this challenge, clustering became a powerful data gathering in IoT applications that saves energy by organizing IoT nodes into clusters. The Cluster Head (CH) oversees all Cluster Member (CM) nodes in each group allowing for the creation of both intra-cluster and inter-cluster connections. There are many algorithms to improve the lifespan of the network, increase the number of active nodes, and extend the remaining energy time in IoT. These algorithms employ techniques such as clustering and optimization to enhance both the energy efficiency and overall performance of the network. In this paper, Low Energy Adaptive Clustering Hierarchy (LEACH), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (AFSA), Energy-Efficient Routing using Reinforcement Learning (EER-RL), and Modified Low Energy Adaptive Clustering Hierarchy (MODLEACH) algorithms will be studied and MATLAB code will be implemented, tested, and the results will be validated.