Soil erosion poses a fundamental threat to sustainable development in Sub-Saharan Africa (SSA), where similar to 490 million hectares of agricultural land are already degraded. Here we present a PRISMA-guided systematic review of 399 peer-reviewed studies (2000-2024) that evaluates the applicability, performance, and policy relevance of geomorphometric soil erosion models in SSA's data-scarce, topographically complex landscapes. Empirical models (chiefly RUSLE and its derivatives) dominate the literature similar to 68 %, followed by physically-based (WEPP, KINEROS2, EUROSEM) and emerging hybrid/machine learning frameworks. Although high resolution DEMs and multi-temporal satellite imagery (Landsat and Sentinel) have markedly improved spatial prediction of erosion hotspots, critical limitations persist: (i) widespread absence of field based-validation, with most studies relying on indirect proxies or qualitative checks; (ii) underrepresentation of Central and Southern African semi-arid catchments, producing a strong East African bias and (iii) negligible integration of model outputs into national or sub-national land use policy, spatial development frameworks, or climate adaptation strategies. Hybrid and data-driven models consistently outperform traditional empirical and process-based approaches (mean AUC 0.83-0.86 vs. R & sup2; similar to 0.55 for RUSLE type models), yet their adoption remains marginal owing to calibration and computational barriers. We identify an urgent need for regionally calibrated ensemble models, continent-wide validation networks using UAV/LiDAR ground truthing, and institutional mechanisms to translate geomorphometric risk maps into actionable policy. To counter these gaps, there is a need to deliver reliable, policy-ready erosion assessments capable of guiding sustainable land management and safeguarding agricultural productivity in one of the world's most erosion vulnerable and food insecure regions.