In this paper, the author presents RetailNet, a reinforcement learning framework based on AI, which is utilized to enhance sales patterns and marketing techniques of perishable products in retailing, dynamic pricing, inventory optimization, and the arrangement of products on shelves are combined with customer behavior and product perishability, modeling the complex relationships with the help of a Pair-Wise Multi-Q Network. This model is furthered with RetailNet++ that assists in promotional strategies to a multi-action Q-aggregation mechanism. This mechanism analyses in various customer behaviors and demand trends that our framework has continually helped to increase revenue as well as demand. Operational efficiency. These outcomes will furnish us with the potential of Ai-improved retail systems to make effective choices and offer smarter in the real world retail settings.