Meta-analysis of multicomponent risk prediction models reveals inconsistent performance in identifying stroke survivors likely to develop cognitive impairment, dementia, or delirium. The analysis examined models incorporating multiple clinical variables rather than single cognitive scores, finding variable discriminative accuracy across different prediction tools and populations. This variability suggests current risk stratification methods may inadequately capture the complex interplay of factors determining post-stroke cognitive trajectories. The findings highlight a critical gap in precision medicine for stroke recovery, where approximately 30% of survivors develop some form of cognitive syndrome. Effective risk prediction could enable targeted interventions during the crucial early recovery window when neuroplasticity is highest and preventive strategies most impactful. However, the heterogeneity in model performance indicates that one-size-fits-all approaches may be insufficient. Future development should focus on population-specific models that account for stroke subtype, location, severity, and individual patient characteristics. The inconsistent results also underscore the need for larger validation cohorts and standardized outcome definitions. Until more robust prediction tools emerge, clinicians should maintain heightened surveillance for cognitive changes in all stroke survivors rather than relying solely on risk scores for screening decisions.