Abstract: This study presents a novel framework for optimizing HIV clinic operations with a focus on enhancing the delivery of pre-exposure prophylaxis (PrEP) services. The research uses advanced process mapping, synthetic data modeling, and visual analytics to investigate and identify operational inefficiencies across the PrEP care continuum from patient intake and risk assessment through prescription, adherence monitoring, and post-treatment follow-up. Instead of relying on conventional surveys, the study generates synthetic datasets to simulate staff satisfaction, clinic throughput, and error rates under different workflow configurations. These simulations are visualized through intuitive yet uncommon formats, including bar charts, radar charts, and swimlane Gantt charts. Findings reveal that workflow redesigns can significantly improve staff-reported task clarity, reduce clinical errors, and enhance operational efficiency without increasing resource burdens. The approach offers a scalable, low-risk, pre-implementation diagnostic tool for health systems aiming to integrate PrEP into diverse care settings. By combining implementation science with systems engineering, this study contributes a practical and replicable model for improving the structural quality of HIV prevention services.