This study presents a bibliometric analysis and trend of a research field, involving optimization of microstructures using an artificial intelligence (AI) application, known as AI-driven microstructure optimization research. The bibliometric analysis evaluates 971 academic publications from 2015 to 2024, using the VOSviewer software and the Scopus database. The analysis examined keyword distributions, researcher collaborations, citation networks, and publication trends, tracking the evolution of research in this field. The results indicate a steady increase in research related to Microelectromechanical Systems (MEMS), with China and the United States leading in publication volume and citations, especially AI-driven microstructure optimization. Machine Learning (ML) and Deep Learning play a crucial role in microstructure optimization, contributing to enhancing efficiency and improving microstructure performance. The keyword network analysis identified four main research clusters, reflecting interdisciplinary applications in neural networks, microstructure, MEMS, and optimization. These findings provide valuable insights for researchers to identify key trends, avoid redundant studies, and explore new directions for MEMS research with AI applications. Additionally, this analysis highlights the increasing role of AI as a transformative tool in microstructure optimization, enhancing efficiency and structural performance at the micro-level.