Abstract Kenya experiences a critical firearm management gap, evidenced by a 70% surge of illegal firearm recoveries and a reliance on fragmented manual record keeping. This study developed the Proposed Geolocation Mapping Model (PGMM), a context-aware IoT framework designed to transition Kenyan security forces to an automated, real-time oversight. Using a Design Science Research paradigm grounded in Socio-Technical Systems Theory and TAM, the study employed a multi-method approach: thematic analysis of 18 expert consultations, participatory workshops with 24 stakeholders, and machine learning simulations. The PGMM integrates biometric authentication with hybrid GPS/cellular geolocation using dual Random Forest classifiers, Owner Identification (OIC) and Location Identification (LIC), fused via a novel AND-gate decision module. The simulation results identified a high internal consistency, with the OIC achieving 93.0% accuracy (AUC = 0.97) and the LIC achieving 87.5% (AUC = 0.81), both significantly exceeding pre-specified global field benchmarks. A Wilcoxon signed-ranks test confirmed OIC superiority while a Kruskal-Wallis test (p =.048, η2 = 0.41) showed that there existed a significant perception gap, as technology specialists’ ratings implied a high system feasibility than security practitioners’ ratings. These findings show that lightweight IoT data can effectively replace bandwidth-heavy video for firearm management in infrastructure-constrained environments. While the simulation validated the architectural construct, field implementation requires addressing stakeholder perception gaps and doing cellular coverage audits to maintain the LIC’s performance advantage. The PGMM provides a statistically evidenced foundation for transforming firearm management from passive record-keeping to high-assurance, real-time geofencing. Keywords: IoT, biometric authentication, geolocation, Random Forest, firearm management, Design Science Research, decision fusion.