Urban change detection is highly crucial for monitoring changes in land cover, growth in infrastructural development, change in land use, and water availability. Traditional approaches are very time-consuming and resource-intensive since much relies on human intervention. The model presented here forms a semi-automatic change detection model that is based on two different timelines, input with the application of multispectral images from Landsat-8. It computes NDVI, NDWI, and NDBI indices in order to categorize the land cover into vegetation, forest, water, built-up areas, and barren land using the help of remote sensing, and after that, differentiation is done on pixels to show the major changes, eventually generating a binary change map and a change matrix for measuring the percentage of change in land cover over time. This methodology reduces the time of analysis and relies less on human resources to enhance the efficiency of decision-making in urban planning and management. The area of study for this analysis is Penamaluru, which is a constituency located in Vijayawada, Andhra Pradesh, India, covering an area of 6.68 square kilometers. The study observes changes in land cover over the years from 2016 to 2023, with dramatic changes over the different land cover types. Notably, built-up area increased by a good percentage from 1.74% to 4.50% corresponding square kilometers from about 0.078 to 0.301 square kilometers) and vegetation coverage was low at 50.07% but decreased further at 47.22%, (about 3.345 square kilometers to 3.157 square kilometers). These changes reflect a rapidly urbanizing zone within the area.