The income inequality-FDI nexus is ambiguous on theoretical grounds, as several opposing mechanisms relating FDI to inequality are put forward in the theoretical literature. Empirical studies also produce mixed results. It might suggest a heterogeneous response of income inequality to FDI inflows conditional on distinct characteristics in recipient countries. Although unobserved country-specific characteristics are considered by fixed or random effects modeling in panel regression applications, some studies incorporate observable country-specific factors such as absorptive capacity as a conditioning factor to explain the reasons for conflicting findings. Therefore, the existing studies employ threshold panel regression models that split the sample based on the outcome (supervised learning). Differently from the previous ones, this study takes a distinct empirical strategy by adopting a finite mixture model (FMM) as an unsupervised model-based clustering technique to scrutinize distributional heterogeneity in the linkage between FDI and income inequality. The study then questions the role of absorptive capacity as a conditioning factor with varying effects on the inequality of FDI. Our empirical results, based on panel data from 26 developing countries between 2004-2019, explain the varying effects of FDI on income inequality according to three different country clusters. FDI improves income inequality in the first cluster, while it does not significantly affect income inequality in the second and deteriorates income inequality in the third cluster. Furthermore, our empirical findings reveal that a country's high absorptive capacity, especially its high level of human capital, prevents the negative impact of FDI on income distribution.