Abstarct: ct This study aims to estimate the household income function in urban areas of Iran and investigate the factors affecting household income levels. The main research issue is to identify and accurately quantify the impact of various socio-economic and demographic variables on household income levels in these areas in order to understand the existing income inequalities and provide appropriate policy solutions for them. To answer this question, a combined approach consisting of three main methods has been used: first, regression analysis to model the relationships between household income and key independent variables; second, analyzing household survey data to obtain valuable information about the factors affecting income and existing gaps; and third, applying advanced machine learning techniques to analyze large datasets, with the aim of discovering more complex and detailed patterns that may not be identified by traditional methods. The results of this study clearly show that factors such as education, employment status, household size, and location are among the most important and influential variables in determining household income levels. These findings provide a powerful tool for policymakers to design and implement targeted interventions—including educational programs and employment promotion—to improve household income levels, reduce poverty and income inequality, and ultimately enhance overall economic well-being in urban areas of Iran.