With the expansion of agricultural production scale and the intensification of farming practices, the threats posed by pests and crop yield and quality diseases have become increasingly severe. Traditional pest and disease monitoring and control methods often fail to meet modern agriculture’s demands for efficiency and precision due to issues such as delayed detection and excessive pesticide use. This paper systematically analyzes the current applications and development trends of artificial intelligence (AI) technologies in pest and disease monitoring and control in agriculture. Leveraging image recognition technology, AI enables efficient identification and automated monitoring of crop diseases and pests, supporting real-time early warning systems. Combined with big data analysis, AI can also provide precise control strategies, optimize pesticide usage, and reduce environmental pollution. Additionally, the potential of AI in biological control and organic farming is noteworthy, contributing to sustainable agricultural development. Although AI faces challenges in technical, ethical, and social dimensions in pest and disease monitoring and control, its development potential remains immense. This paper aims to explore the technical principles, practical applications, and future directions of AI in agricultural pest and disease monitoring and control, providing insights for further research and application in this field.