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Résumé : This study examines, within an analytical and holistic framework, how artificial intelligence (AI) and machine learning-driven algorithmic pricing systems are reshaping pricing behavior in international trade, firms’ profit margins (markups), and the broader dynamics of global competition. It focuses on three main research questions: the forms of market power generated by algorithmic pricing in export markets, the ways in which international organizations address the risks of digital monopolization and tacit collusion associated with autonomous pricing systems, and the regulatory gaps embedded in existing global trade rules. Methodologically, the study adopts a qualitative research design based on a systematic document analysis. In this context, academic studies, official reports published by international organizations such as the OECD, WTO and UNCTAD, as well as macroeconomic trade data, were examined through thematic analysis. The findings indicate that AI-based pricing systems provide firms with significant efficiency gains by reducing transaction costs, increasing operational speed, and enabling highly accurate demand forecasting. However, these autonomous systems may facilitate near-perfect price discrimination, price convergence, and disproportionate market concentration in target markets. The analysis also suggests that the digital divide between technologically advanced economies and developing countries is becoming increasingly pronounced. Overall, the study argues that, in order to prevent such algorithmic systems from undermining global welfare, existing competition law frameworks must be urgently revised in line with a human-centered, fair, and transparent approach to digital governance.