Land Use and Land Cover (LULC) change detection is a critical component in understanding the dynamics of landscape transformations and their impact on environmental sustainability. This chapter provides the history and methodologies associated with LULC change detection using Remote Sensing (RS), Geographic Information Systems (GIS), and Machine Learning (ML) techniques. It covers the evolution of LULC change detection methods, from traditional techniques to the integration of ML algorithms in recent years. The chapter delves into the principles and applications of both supervised and unsupervised machine learning models for LULC classification, highlighting their strengths and limitations. A case study on the Upper Bhavani Basin for the period 2017–2021 demonstrates the practical application of these methods, utilizing Landsat 8 images and the Random Forest classifier in Google Earth Engine (GEE). The study assesses the accuracies and Kappa coefficient values to evaluate the performance of the model.