Researchers analyzed 215 studies examining artificial intelligence applications in diffuse large B-cell lymphoma (DLBCL) prognosis, revealing that machine learning models consistently outperform traditional risk assessment tools like the International Prognostic Index across multiple data types including imaging, genomics, and clinical parameters. This systematic review represents a significant milestone in precision oncology, demonstrating that AI-driven approaches can extract prognostic insights from complex, multi-dimensional cancer datasets that conventional statistical methods miss. The implications extend beyond DLBCL to other hematologic malignancies where current prognostic tools lack individual-level precision. However, the field faces substantial implementation barriers including model validation across diverse patient populations, regulatory approval pathways, and integration into existing clinical workflows. Most concerning is the potential for algorithmic bias if training datasets lack demographic diversity. While promising, these AI models require extensive prospective validation before clinical deployment, and their complexity may limit physician acceptance. The convergence of multiomics data with advanced machine learning suggests we're approaching a paradigm shift toward truly personalized cancer prognosis, though the timeline for widespread clinical adoption remains uncertain.