This research aims to optimize ground support systems for underground tunnels in geologically challenging environments, specifically addressing the reduction of Fall of Ground (FOG) incidents in a gold mine in Mashava, Zimbabwe. The study integrates advanced detection and classification methodologies to enhance tunnel stability and safety. Tunnel Reflection Tomography (TRT) was employed to identify unfavorable geological structures ahead of excavation, while core logging at 20 locations on level 7 provided rock mass quality assessments using three classification systems: Bieniawski’s Rock Mass Rating (RMR), Laubscher’s Mining Rock Mass Rating (MRMR), and Barton’s Q-system. The results consistently indicated poor rock mass quality, informing the design and refinement of a robust ground support system. Fallout height data from past FOG incidents and probabilistic key block analysis using J-Block software further validated the support system's effectiveness. The findings significantly reduce collapse risks and downtime, enhancing operational safety and efficiency. This research contributes to developing practical strategies and tools for improving tunnel stability in complex geological settings, offering valuable insights for future advancements in mining support technologies. The study's necessity stems from the industry's growing demand for innovative solutions to enhance tunnel stability in adverse geological settings, particularly in regions with limited access to advanced technologies or methodologies.