<p>This study examined the dynamics of land use and land cover (LULC) changes and their effect on land surface temperature (LST) in Vientiane capital, Lao People’s Democratic Republic (PDR), over the period from 2000 to 2024, utilizing machine learning (ML) algorithms within the Google Earth Engine (GEE) platform. Three different algorithms (Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART)) were assessed for classification accuracy. Among these, RF proved to be the most effective, achieving an overall accuracy of 95.34% and a kappa coefficient of 0.943, followed by SVM. The analysis revealed a significant growth in all land cover classes, with the exception of forested areas, which indicates the ongoing process of urban expansion and the conversion of forested land to other economic uses within the study area. In addition, the study assessed changes in LST and found a temperature rise of about 3.47&#xa0;°C during the study period, with urbanized surfaces exhibiting the highest temperatures due to the urban heat island (UHI) effect. A strong positive correlation between LST and urbanized areas was identified, while vegetation and water bodies exhibited a cooling effect, demonstrating the critical role of green and blue infrastructure in moderating urban temperatures. These findings highlight the importance of adopting sustainable urban planning strategies in rapidly urbanizing regions. The policies that promote the development of green spaces, urban greening, and water conservation initiatives could help alleviate the rise in temperatures and improve ecological resilience. This research provides valuable insights for urban planners and policymakers aiming to integrate economic growth with environmental sustainability, offering a foundation for data-driven approaches to urban land management.</p>