The rapid growth of Internet of Things (IOT) technologies and the increasing demand for energy efficiency have led to the emergence of smart buildings, which integrate advanced sensors, IOT devices, and machine learning algorithms to optimize energy consumption and improve occupants' comfort. This study aims to provide a comprehensive analysis of the latest IOT-based technologies and machine learning techniques used for enhancing energy efficiency in smart buildings. The paper will investigate the various types of sensors and IOT devices employed for data collection, examine the role of machine learning algorithms in analyzing and predicting energy consumption patterns, and discuss the challenges and opportunities associated with the implementation of such technologies in the built environment. Additionally, the study will present case studies of successful IOT-based energy management systems in smart buildings, highlighting the key factors that contribute to their success and the lessons learned from these implementations. The findings of this research can serve as a valuable reference for practitioners, researchers, and policymakers working in the field of smart buildings and IOT, helping them better understand the potential benefits and challenges of implementing IOT-based energy management solutions in the built environment.