Climate change and diverse environmental stresses (such as drought, high salinity, and temperature fluctuations) pose significant challenges to the sustainable development of agriculture, severely affecting crop growth, yield stability, and global food security. As a result, improving crop stress resistance and environmental adaptability has become a core topic in modern agricultural research. Artificial intelligence (AI), a cutting-edge technology, has demonstrated immense potential in enhancing crop stress resistance due to its powerful data analysis and pattern recognition capabilities. By integrating genomics, phenomics, and environmental data, AI technology can efficiently screen stress-resistant genes, decipher complex gene networks, and, when combined with gene editing techniques, precisely improve crop traits. Moreover, machine learning and deep learning algorithms play a crucial role in phenotype data processing, multi-trait optimization, and the design of breeding strategies for environmental adaptability, significantly enhancing breeding efficiency and precision. This review systematically summarizes the latest applications of AI in improving crop stress resistance and environmental adaptability, focusing on key technologies such as AI-assisted stress gene identification, gene editing optimization, and phenotypic data mining, along with practical case studies. The article also discusses the main challenges and technical bottlenecks in current research and looks forward to the future development of AI in intelligent breeding. By exploring AI-driven new breeding models, this paper aims to provide theoretical foundations and technical references for agricultural technological innovation and sustainable development.