WSN is a low-power system and are often used in numerous monitoring uses, such as healthcare, environmental, and systemic health surveillance, in addition to military surveillance. It is important to reduce network resource usage since many of these applications need to be installed in locations that are virtually inaccessible to humans. Many protocols for WSN to extend the presence of the network have been established to solve this problem. In the energy efficiency of WSN networks, routing protocols play an important role since they help minimize power usage and response time and provide sensor networks with high data density and service quality. This study also employed a Hopfield neural network and the findings from this study are presented next to each other to enable comparison. This paper also discusses how to easily and accurately capture and handle WSN collisions. Future experiments that require the usage of neural networks and so many fuzzy structures will be able to prevent a crash in these respects.