<p>Identifying changes in land use and land cover is of utmost importance in the context of land use planning and the sustainable management of land resources. A case study is performed using the Sentinel 2 images of the location around the city of Ernakulam under the jurisdiction of the state of Kerala in India. Machine learning models assisted with hyperparameter optimization techniques are used for examining and tracking changes in land use and land cover across both space and time. In particular, we used random forest aided by an evolutionary-based hyperparameter optimization model to classify the land use land cover pattern. The hyperparameters of the random forest classifier are tuned using advanced techniques including Bayesian Optimization and its variants employing Differential Evolution and Evolutionary Strategies, to enhance model accuracy and generalization. These methods allowed for efficient exploration of the hyperparameter search space, contributing to improved classification outcomes. A post-classification change detection analysis is carried out on Sentinel-2 images to create maps and analyze the scale and pace of changes in urban growth within the area post COVID-19 pandemic.</p>