This study was undertaken to contribute further to the body of knowledge of shadow economy studies, by investigating how the scale of the shadow economy influences income inequality in the context of a developing country, with Malaysia serving as the case study. Utilizing annual data from 1980 to 2018, the analysis draws upon four distinct shadow economy estimations: (i) the Multiple Indicator-Multiple Cause (MIMIC) model-based figures presented by Medina and Schneider (2019); (ii) the estimates developed by Elgin, Kose, Ohnsorge, and Yu (2021), which include both (a) MIMIC-based and (b) Computable General Equilibrium (CGE) model approaches; and (iii) the Modified-Cash-Deposit-Ratio (MCDR) method estimates provided by Habibullah, Din, Yusof-Saari and Baharom (2016). Gini coefficient is employed as the measurement for income inequality. The empirical framework integrates real Gross Domestic Product (GDP), tax burden, and urbanization growth as control variables. To evaluate the nexus between the shadow economy and income inequality, the study employs three estimators namely: Ordinary Least Squares (OLS) with robust standard errors, Robust Least Squares (RLS) incorporating M-estimators, and Fully Modified OLS (FMOLS). The analysis conducted are quite robust and comprehensive taking into account both linear and nonlinear dynamics. The empirical evidence predominantly supports a nonlinear relationship, we could see the Kuznet effect, an inverted U-shaped pattern. Implication of the U-Shape is that income inequality initially increases with the expansion of the shadow economy but subsequently decreases once a specific threshold is exceeded. Furthermore, the macroeconomic controls indicate that greater levels of economic development mitigate inequality, whereas higher tax burdens and increased urbanization contribute to its intensification. The analysis also uncovers nonlinear effects in the relationships between inequality and both tax burden and urbanization.