Classifying land use and land cover (LULC) is a fundamental process in remote sensing and geographical information systems (GIS) that is essential to many applications, including disaster assessment, urban planning, environmental monitoring, and natural resource management. Understanding the dynamics of landscapes and how they evolve over time requires accurate classification of land use and land cover groups. For this reason, straightforward classification methods like decision trees, artificial neural networks and maximum likelihood have historically been employed extensively. However, there has been an increasing interest in investigating machine learning techniques' potential to enhance the precision and effectiveness of LULC classification since their introduction. Computer vision, natural language processing, and remote sensing are just a few of the fields that have greatly benefited from the quick development of machine learning algorithms, especially deep learning approaches. Due to its capacity to automatically extract intricate patterns and features from massive datasets, machine learning-based techniques have become more and more popular in LULC classification jobs in recent years, potentially surpassing conventional approaches. This research paper aims to conduct a Land use classification by using machine learning based (ML) models (Support Vector Machine (SVM) model, Random Forest (RF) and Decision Trees (DT) models) with the use of open-sourced Python modules (Rasterio, Numpy, and Scikit-learn). The comparative analysis demonstrates that the SVM model achieved the highest performance with an Overall Accuracy (OA) of 97.30%, followed by Random Forest at 94.59%, and Decision Tree at 89.19%.