Abstract The small ones are the general non-uniformity of the hierarchical structure of machine learning, networking, communication, such as learning machine performance to evaluate the Internet of Things (IOT) performance and various devices in the green building of things. There is a big difference between those who want to evaluate the Internet of Things (IOT) fast connection to the Internet. Due to the energy of IoT sensors, the current consumption of green buildings around the world is significantly equal to one-third of all available primary energy resources. Many green buildings, energy and environmental aspects must be considered, which complicates the performance assessment of the overall IoT system of green buildings. This is due to the effects of manufacturing in climate variability and environments such as soil, air and water. The location of temporary facilities for green building pollution and the layout of the entire site directly affect project goals such as time, labor costs, material transportation and processing. A detailed study of the use of machine learning planned layout sites and environmental performance assessments was conducted in terms of project goals and website layout sustainability performance. This proposed system is designed for support machine learning and the internet of things, green, it is possible that the internet goal set.The purpose of developing a system of things to improve the performance evaluation system has not been achieved. To assess sexual risks, therefore, analyze the influencing factors and impact environmental performance Machine learning and sustainable development and green building site layout. The system integrates qualitative and quantitative attributes to provide a comprehensive assessment of the environmental benefits of green construction site layouts.