Wireless sensor networks often face a significant challenge due to their limited energy capacity. To address this challenge, numerous studies are currently underway to explore innovative ways to optimize energy usage. This study proposes an energy-efficient cluster head selection method that is supported by artificial neural networks for cluster-based sensor networks, thereby enhancing their energy efficiency. The neural network is placed in each sensor node. The nodes decide to be the cluster head according to the threshold they set using the ANN, without the need for centralized control. According to the simulation studies conducted, the ANN trained with 5-5-5 neurons survived for 3497 rounds and successfully transmitted 96757 packets to the sink. In contrast, the LEACH algorithm could only survive for 1773 rounds and transmitted 61766 packets to the sink under the same conditions. The study confidently concluded that there was a significant and noteworthy improvement in both the network lifetime and the number of packets sent to the base station. On the other hand, it was also observed that the proposed method showed a notable improvement when compared with similar recent studies conducted with the same parameters by a factor ranging from 3.63 to 6.26.