Accurate classification of Land Use and Land Cover (LULC) is fundamental to understanding the spatial distribution of natural and anthropogenic features on the Earth's surface. It provides essential insights for urban planning, agricultural development, environmental monitoring, and resource management. The rapid pace of urbanization—particularly in developing regions—has amplified the demand for timely and precise LULC data. Traditional methods, such as manual interpretation and field surveys, are increasingly inadequate due to limitations in scalability, efficiency, and consistency. This study proposes an automated LULC classification approach that leverages deep learning and remote sensing technologies. Utilizing the ResNet50 deep convolutional neural network and the EuroSAT dataset comprising multispectral satellite imagery, the model is trained to classify land cover types such as urban areas, vegetation, water bodies, agricultural zones, and barren land. The classification process involves tiling satellite images into smaller segments, enabling fine-grained spatial pattern detection and high-resolution mapping. The resulting LULC maps visualize land cover categories with color-coded tiles, facilitating rapid and accurate assessments. This approach demonstrates notable improvements in classification speed, accuracy, and consistency, making it suitable for regular environmental monitoring. By integrating artificial intelligence with satellite imagery, the proposed system offers a scalable solution for informed decision-making in land management, sustainability planning, and urban development. As remote sensing data becomes increasingly accessible and frequent, deep learning-based LULC classification systems will play a pivotal role in addressing contemporary environmental and urban challenges.