This study explores the enhancement of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol in wireless sensor networks (WSNs) through the application of three optimization methods: In this study, we adopt Fuzzy Logic, Artificial Neural Networks (ANNs), and a Hybrid Cuckoo Optimization Algorithm with Grey Wolf Optimization (HCOAGWO). We evaluate the performance of these methods using MATLAB simulations in terms of network lifetime, energy efficiency as well as operational effectiveness. The use of a Fuzzy Logic approach to selecting cluster heads dynamically, utilizing the fuzzy sets and rule-based systems; and the ANN-based approach to optimizing data aggregation and transmission processes at cluster heads. An HCOAGWO algorithm, inspired by Cuckoo Search and Grey Wolf Optimization, is proposed to improve cluster head selection and communication strategies. Simulation results show that in all tested scenarios, the HCOAGWO method outperforms Fuzzy Logic and ANN. In particular, HCOAGWO attains the highest values of performance metrics for the least number of rounds until the first node dies and the largest number of rounds until 80 and 50% of the nodes are live. The network lifetime is considerably enhanced under HCOAGWO: while First Node Death (FND) a round refers to one complete cycle of operations, including cluster head selection, data aggregation, and data transmission from sensor nodes to the base station or sink. Each round measures the collective process of nodes gathering, processing, and transmitting data, which consumes energy and affects the network's overall lifespan, and accounts for 1050 rounds in HCOAGWO; 80% of the nodes remains live for 1800 rounds and 50% for 2200 rounds under the LEACH protocol. This is edged out of the charts by Fuzzy Logic and ANN edges in the same metrics. This work has proved the efficiency of HCOAGWO in optimizing WSNs for robust solutions that are aimed at increasing network longevity and energy efficiency.