The urban thermal environment is a comprehensive physical environment system that integrates various external factors related to heat within a city, which affects the survival and development of its residents. Thermal environment is regarded as one of the most important ecological parameters in both natural and urban ecosystems. It is well established that obtaining adequate space-time-related thermal environment parameters is a prerequisite for conducting urban thermal environment studies. However, the stations of urban meteorological stations managed by government departments are relatively scattered, and the data obtained cannot express the microclimate conditions within the city. Scholars can obtain rich data on urban thermal environment parameters by independently setting up on-site monitoring points, but this requires significant economic costs. Currently, domestic and foreign scholars have achieved the goal of efficient monitoring of the ecological environment using Internet of Things (IoT) technology. Monitoring urban thermal environment by using Internet of Things (IoT) technology can effectively address these issues. In this study, we selected Guangzhou University as research area and developed a series of innovative thermal environment field measurement devices based on Internet of Things (IoT) technology to collect air temperature, wind speed, solar radiation, and land surface temperature simultaneously online. Specifically, ten measuring sites were selected to record the four thermal environmental parameters mentioned above between October 16 and 24, 2022. The results were as follows: 1) The two boundary parameters, solar radiation, and wind speed shaped the basic characteristics of the thermal environment in the study area. There was a strong correlation between the wind speeds of different sites, while very few sites had very low correlation between the wind speeds, indicating the uniqueness of the microclimate. 2) The variations of wind speed and heat island intensity at different sites were significantly different both temporally and spatially. Even with the close distance between measurement points, the characteristics of the thermal environment were influenced by the surrounding buildings and vegetation. The wind speed and heat island intensity were higher at each measuring site during the daytime and vice versa at night. 3) The correlation between land surface temperature and air temperature was more than 0.8, and this correlation was stronger at night than during the day. Interestingly, this relationship was also not greatly influenced by wind speed. The results of this study revealed the high heterogeneity of the urban thermal environment at a local scale and demonstrated the feasibility, convenience, and efficiency of IoT technology for urban thermal environment monitoring.