Due to their numerous uses in industries like industrial automation, healthcare, and environmental monitoring, Wireless Sensor Networks (WSNs) have drawn a lot of attention. The main method for improving the effectiveness and scalability of WSNs is clustering. Using Artificial Intelligence (AI) methodologies, we explore the design and optimization of clustering algorithms in WSNs in this study. We investigate how AI algorithms can enhance the cluster formation, cluster head selection, and performance optimization processes. Our research focuses on the use of AI-driven clustering to improve data aggregation, network lifetime, and energy efficiency. We provide insights into the most cutting-edge AI-based clustering techniques and their effect on WSNs by a thorough examination and analysis of the available research. We also suggest future topics for research in order to develop AI-driven clustering methods in WSNs. In Wireless Sensor Networks, the combination of Artificial Intelligence and clustering techniques has shown to significantly improve data aggregation, energy efficiency, and overall network performance. The development of more sophisticated and adaptable clustering approaches that meet the particular difficulties faced by WSNs presents an interesting potential as AI technologies advance, ultimately assisting in the effective operation of diverse real-world applications.