Purpose /Significance To systematically review the research progress of artificial intelligence (AI) in the prediction of glioma disease trajectory, and to provide new perspectives and ideas for innovation and clinical transformation in this field. Method /Process The application of different types of AI technologies in the diagnosis and treatment decision-making and prognosis assessment of glioma are analyzed. The characteristics of three multimodal data fusion strategies are compared, and the challenges faced by AI technologies in the clinical transformation process are discussed. Result /Conclusion AI leveraging multimodal data fusion can significantly enhance the performance of glioma disease trajectory prediction. In the future, high-quality standardized disease-specific datasets should be constructed, interpretable fusion algorithms should be developed, new dual-driven paradigms integrating data and knowledge should be explored, and cross-institutional data compliance sharing and privacy protection should be strengthened to promote the application of related technologies in precision medicine for glioma.