Strain screening and optimization have significant applications in agriculture, environmental remediation, and industrial production. However, traditional methods are often constrained by high costs, low efficiency, and insufficient accuracy in complex environments. The rapid advancement of artificial intelligence (AI) offers innovative solutions to these challenges. Through machine learning and deep learning algorithms, AI can efficiently predict the functional traits of strains from vast experimental data, optimize culture conditions, and significantly enhance the efficiency and accuracy of high-throughput screening. This paper systematically reviews the technical framework of AI in strain screening and optimization, discusses its key applications in optimizing culture conditions and high-throughput screening, and explores its potential value in cutting-edge fields such as strain design, synthetic biology, and gene editing. Additionally, this paper analyzes the main limitations of AI technology in strain research, including insufficient data quality, poor model interpretability, and ethical and regulatory challenges. Finally, future research directions are proposed, along with potential solutions, to provide valuable academic references for the AI-driven strain screening and optimization field.