Abstract: Prostate cancer is the second most commonly diagnosed malignancy in men worldwide and a leading cause of cancer-related mortality in sub-Saharan Africa. Men of African ancestry experience disproportionately higher incidence and disease aggressiveness compared to their European counterparts yet remain historically underrepresented in large-scale genomic studies. We present an interpretable clinical decision support dashboard for prostate cancer risk stratification, trained on 113 labelled patients from the South African Prostate Cancer Study (Jaratlerdsiri et al., Nature 2022). Using whole-genome sequencing-derived features spanning clinical, molecular, and genomic domains, we trained a Random Forest classifier to predict cancer aggressiveness based on the ISUP Grade Group, binarised as low-risk (Grade Groups 1-3) versus high-risk (Grade Groups 4-5). The model achieved a weighted F1-score of 0.77, a precision of 0.77, and a high-risk recall of 86.7%. Feature explainability analysis identified log-transformed PSA, Tumour Mutational Burden, Genomic Molecular Subtype, Age, and Driver mutation counts as the dominant predictors. The resulting dashboard integrates cohort-level visualisations, classifier diagnostics, and a live individual patient explorer with counterfactual reasoning, providing clinicians with an auditable, context-aware tool tailored to the South African clinical setting.