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Surgical site infections (SSIs) remain a significant source of postoperative morbidity despite established guidelines for perioperative antibiotic prophylaxis. Current protocols emphasize fixed preoperative timing and interval-based intraoperative redosing, yet fail to account for patient heterogeneity, pharmacokinetic variability, and uncertainty in procedure duration. This study proposes a hierarchical reinforcement learning (HRL) framework for personalized optimization of antibiotic prophylaxis across the perioperative timeline. The framework decomposes decision-making into two coordinated levels: a high-level policy that determines optimal preoperative antibiotic timing based on predicted procedure duration and patient-specific infection risk, and a low-level policy that adaptively manages intraoperative redosing using real-time updates on elapsed time, remaining duration, and cumulative drug exposure. Procedure duration is estimated using machine learning models that provide both point predictions and uncertainty intervals, enabling risk-sensitive decision-making. The problem is formalized as a Markov decision process with a reward structure balancing SSI prevention against antibiotic stewardship, incorporating penalties for unnecessary dosing and suboptimal timing. Off-policy evaluation using historical surgical data is proposed to assess performance relative to guideline-based and clinician-driven strategies. By integrating predictive modeling with multi-timescale decision optimization, the framework aims to reduce SSI incidence while minimizing antibiotic overuse. This approach highlights the potential of reinforcement learning to advance precision perioperative care and improve clinical outcomes through adaptive, data-driven prophylaxis strategies.