In the field of Business Administration, Management and Corporate Governance, static retail pricing and disconnected channel inventory cause severe stockout penalties and excessive markdown write-offs. This empirical investigation systematically examines Deep Reinforcement Learning for Real-Time Dynamic Pricing and Inventory Optimization in Omnichannel Retail through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing deep Q-network simulation benchmarked against 1.2 million nationwide retail transaction records across apparel categories, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that algorithmic reinforcement learning pricing expanded gross margins by 4.8% while reducing inventory holding expenses by 12%. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for omnichannel retail executives and supply chain analytics specialists. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.