<p>The fast progress of artificial intelligence (AI) technology transforms industrial efficiency levels and work abilities, significantly affecting worldwide income distribution patterns. This research uses fuzzy-set qualitative comparative analysis (fsQCA) to study the complex mechanisms by which AI affects income inequality across 20 OECD nations from 2015 through 2019. The analysis uses panel data (<i>N</i> = 20, T = 5) to examine three AI-related condition variables and four socioeconomic condition variables that interact with the three outcome variables, i.e., the Gini coefficient, the top 1% income share, and the bottom 50% income share. Our research sequentially evaluates the necessity of individual conditions and the sufficiency of conditional configurations. Specifically, we identify 14 conditional configurations that are classified into seven types of AI-centered pathways affecting changes in income distribution. The study demonstrates that (1) no specific condition operates independently as essential for achieving particular income distribution results, (2) AI produces different effects on income inequality depending on its compatibility with socioeconomic conditions, and (3) there are both similarities and differences in AI’s influence patterns on income distribution at different income levels. For the OECD countries involved and other countries with similar contexts in recent years, the research reveals that policymakers need to implement balanced strategies to use AI to create sustainable social development that remains equitable. Implementing long-term strategies between technological progress and socioeconomic equilibrium should be the top priority for policymakers in these countries to reduce risks of inequality. Despite the data cut-off resulting from keeping the panel data balanced, the methodological strengths of this study ensure its relevance for understanding ongoing trends in AI-related inequality.</p>