We will provide COVID-19 home safety can be improved with a cost-effective IoT-based technology that addresses several critical challenges in this study. Non-contact temperature sensing and the second stage is mask detection. The non-contact sensing element subsystem is driven by an Arduino Uno and utilizes an infrared sensor or a thermal camera, whereas the masking subsystem is powered by a Raspberry Pi. Computer vision algorithms are used on camera-equipped computers to perform detection and social distancing tests. Humans are now utilized for temperature screening and mask spotting in public settings to prevent the spread of COVID-19. All scanning entries include temperature testing equipment; however, manual temperature scanning has several limitations. Temperature scanners are not well-known among personnel. There is room for human mistakes when reading values. Regardless of the higher temperatures or the lack of masks, people are frequently given admission. A piece of manual scanning equipment is ineffective in huge groups. As a response, an automated temperature and mask monitoring system is necessary. We depict a profoundly independent temperature scanner and section source framework to conquer this issue. The innovation takes photographs utilizing a contactless temperature peruse and a camera. The scanner is related with a doorway like contraption that thwarts section. Expecting that a high temperature or the shortfall of a cover is recognized, the gadget uses a temperature sensor and camera related with a Raspberry Pi structure to screen the entire collaboration. The major goal of this article is to automate the entire COVID scanning process to reduce the possibility of COVID-19 spreading in densely crowded locations like shopping malls, schools and colleges.