Urbanization poses significant challenges in many developing nations, including India, where cities are expanding rapidly and often in uncontrolled ways, straining urban planning efforts. While various factors contribute to this growth, transportation networks play a pivotal role. Understanding urban sprawl is vital for future planning, especially in rapidly developing cities. This paper validates use of land use matrices, particularly map density, to analyze urban sprawl using remote sensing and GIS tools. Satellite data from Landsat over the past two decades were downloaded and processed with machine learning algorithms to extract built-up areas. The results highlight the direction and extent of urban expansion, largely driven by development in the transportation network. Additionally, map density spatial distribution analysis and road network buffer analysis were conducted, revealing the intricate relationship between transportation infrastructure and urban growth patterns. However, such unrestricted growth requires mitigation strategies to minimize adverse impacts. Findings from this study would help in future urban and transport planning efforts, promoting sustainable development and mitigating environmental degradation.