Early detection of prostate cancer remains a critical determinant of survival outcomes; however, diagnostic pathways are frequently disrupted among patients with dementia due to delayed symptom recognition, reduced screening participation, and cognitive barriers to healthcare engagement. Although artificial intelligence (AI) has demonstrated strong predictive capability in both oncology and neurodegenerative disease diagnostics, the integration of neurological vulnerability into cancer prediction frameworks remains limited.This study presents a systematic empirical synthesis and comparative machine learning analysis to examine how AI can support earlier prostate cancer risk identification in cognitively impaired populations. Drawing on validated clinical, imaging, and genomic studies, the predictive performance of major classification algorithms—including Random Forest, XGBoost, Support Vector Machine, Logistic Regression, Decision Tree, Artificial Neural Networks, and Gradient Boosting—was evaluated using reported metrics such as area under the curve (AUC), sensitivity, specificity, and accuracy.Across heterogeneous biomedical datasets, ensemble methods consistently exhibited higher predictive stability than standalone classifiers. Random Forest demonstrated strong discriminative capability in biopsy-based cohorts, while boosting algorithms showed robust performance across multimodal clinical indicators. Importantly, emerging evidence suggests that integrating neurological markers with oncological predictors may enhance risk stratification and support anticipatory screening strategies.To address this opportunity, the study proposes a conceptual framework termed Neuro-Oncological Intelligence, which advocates cross-domain predictive modeling as a pathway toward anticipatory precision medicine. Rather than relying solely on reactive diagnostic workflows, such architectures may enable earlier identification of high-risk individuals and improve clinical decision support for medically vulnerable populations.While the findings are evidence-informed and derived from previously validated studies rather than newly trained models, they highlight a promising frontier for translational medical AI. Future research integrating dementia and prostate cancer datasets will be essential to validate these insights and advance real-world clinical implementation.