Urban transition and its environmental implications can be effectively monitored using nightlight intensity, which is a proxy for urban expansion and reflects population density and socio-economic activities. This study effectively captures the spatial and temporal variations in nightlight density across Karnataka for a decade, from 2014 to 2024, using the Vegetation Adjusted Nighttime Lights Urban Index (VANUI). Environmental and socioeconomic variables, i.e., Population Density, Urban Heat Island (UHI) intensity, Land Use Land Cover (LULC), Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Surface Albedo, Relative Humidity, and Specific Humidity, were extensively analysed to determine their correlation with VANUI. A cross-correlation matrix, principal component analysis (PCA), was employed to investigate the dimensionality between the variables and the dominant factors. Furthermore, a regression model utilizing deep learning was developed to predict the nighttime light intensity in Karnataka for 2025. Results indicate that nighttime light intensity is continuously increasing, where population density, UHI, LST, and NDBI are the most influential factors, emphasizing how urban expansion and increasing temperature shape the nighttime illumination. The prediction analysis for 2025 highly indicates how regions are urbanizing, a reflection of the socio-economic transformation, and also, to an extent, how the ecoregion of the Western Ghats is also getting illuminated concerning urban expansion. Future advancement in long-term forecasting using Machine learning (ML) models will provide a deep understanding of urbanization and their environmental impacts.