The application of artificial intelligence (AI) in materials science is driving a paradigm shift, significantly accelerating the discovery, design, and development of new materials. Traditional approaches, which largely rely on empirical intuition and trial-and-error experimentation, are often time-consuming, costly, and limited in their ability to achieve breakthrough performance. In contrast, AI technologies empowered by large-scale datasets, advanced algorithms, and machine learning (ML) techniques enable efficient prediction of material properties, optimization of synthesis pathways, and goal-oriented materials design. This article elaborates on several key domains where AI is making a substantial impact, including high-throughput screening, process optimization, and material characterization and analysis. It also addresses the technical and practical challenges currently impeding broader AI adoption in the field, such as issues related to data quality, model interpretability, and computational infrastructure. Furthermore, the manuscript highlights future opportunities and developmental directions, emphasizing in particular the transformative potential of integrating AI with automated and high-throughput experimentation to advance the widespread and deep application of AI in materials science.