This study addresses critical limitations in contemporary Dutch auction systems through a novel adaptive framework that integrates real-time behavioral analytics with dynamic pricing mechanisms. While traditional implementations employ static, uniform strategies, our approach introduces three key innovations: 1) real-time adaptation of price decrements based on item-specific price-volume relationships and buyer engagement signals, 2) continuous optimization of auction parameters via machine learning, and 3) algorithmic generation of non-linear price reduction patterns to sustain bidder excitement. By bridging auction theory with operational practice, the framework demonstrates significant improvements in transaction efficiency and participant engagement. The system’s price-induced virality mechanism further amplifies auction visibility through strategic triggering of organic social media dissemination. These advancements offer a scalable solution for digital platforms seeking to balance revenue maximization with bidder retention in dynamic markets.