AI-Based Dynamic Pricing: A Cross Industry Bibliometric Review of Trends, Challenges, and Future Directions

Artificial intelligence has transformed dynamic pricing by enabling firms to forecast demand more accurately, respond to market uncertainty, and optimize prices in real time. Existing reviews remain fragmented, typically focusing on a single industry or on isolated methodological streams like reinforcement learning or time-series forecasting. To address this gap, this study provides a comprehensive, cross-industry synthesis of AI-based Dynamic Pricing through a systematic bibliometric analysis of 1301 Scopus-indexed publications from January 2005 to August 2025. E-commerce and digital platforms serve as the study’s central analytical lens because they frequently combine real-time transactional data, rapid price adjustment, customer-level behavioral information, platform competition, and algorithmic repricing. The analysis also extends to other digitally mediated pricing environments, including energy, mobility, electric-vehicle charging, hospitality, transportation, and retail, allowing the study to examine how methods, adoption patterns, and governance concerns vary across sectors. Using VOSviewer and CiteSpace, the study maps the intellectual structure of the field and identifies eight major research clusters. The findings reveal a clear methodological shift from rule-based and econometric approaches toward deep learning, multi-agent reinforcement learning, and simulation-driven decision systems. They also show that data-intensive and platform-mediated sectors are becoming increasingly prominent in the development and application of advanced AI-based pricing methods, while established revenue-management domains such as airlines and hospitality remain important foundations of the field. Building on these patterns, the study outlines future research opportunities centered on interpretable and uncertainty-aware pricing models, ethical and fair pricing mechanisms, and cross-industry transfer of methods and regulatory practices. This synthesis provides a structured foundation for advancing theory, methodology, and practice in AI-based DP.