Accurate classification of land use and land cover (LULC) is crucial for environmental monitoring, urban planning, and climate change studies. Traditional methods, such as manual interpretation, are labor-intensive and subjective. Deep learning offers automated solutions; however, challenges like data scarcity and model interpretability remain. This study investigates deep learning for LULC using satellite imagery, comparing architectures and preprocessing impact. While deep learning offers scalability and accuracy, addressing challenges is key to robust results. This chapter aims to advance automated LULC classification and provide insights for future studies. The limitations of current LULC methods include a lack of integration of advanced deep learning (DL) models, training on region-specific datasets leading to biased performance, and underrepresentation of certain LULC classes. To address these limitations, this study utilizes the Sentinel-2 LULC dataset, consisting of over 213,750 pre-processed images at 10 m resolution representing seven distinct classes of LULC. These classes encompass water, dense forest, sparse forest, barren land, built-up areas, agricultural land, and fallow land, thereby representing a diverse and versatile region. Six deep learning models are employed for training and testing: LinkNet Inceptionv3, LinkNet ResNet152, UNet ResNext101, UNet Inception ResNetv2, UNet DenseNet169, and UNet EfficientNetb7 with accuracies ranging from 92–96%. The performance of these models is compared to determine the most suitable model for LULC classification, which can subsequently be employed for further route planning applications. Through this comparative analysis, insights into the efficacy of different DL architectures and preprocessing techniques are provided, contributing to the advancement of automated LULC classification methodologies.