Economic development and urbanization depend highly on traditional metrics that provide little geographic specificity and delayed reporting times that hinder continuous monitoring. This paper proposes a method to automate the prediction of economic conditions by machine learning models, together with the Internet of Things (IoT) sensors and satellite observations of night lights. Combining a refined data pipeline and its stages, including data transformation, with the generation of features and clustering, followed by predictive modeling, provides robust analysis. New indicators such as light density and light per person support the spatial and temporal analysis. The K-means algorithm distinguishes five clusters ranging from entirely rural to completely urban. Regression models, including linear regression with random forest and gradient enhancement, and XGBoost and CatBoost yield a r2 of 95.21%. The suggested work provides evidence of the increased urbanization and changes in social structures and political developments. The method of analysis speeds up and improves the accuracy of forecasting urbanization patterns in conjunction with economic developments, thus providing valuable information for urban planners and policymakers.