The world around us is rapidly evolving, and this unrelenting progress will inevitably have some sort of impact on human health. As the world's population rises, the negative health effects of new technologies and infrastructure will inevitably rise alongside it. Invisible pollutants, like smoke, must be tracked down in order to be dealt with effectively. A model that can identify hazardous contaminants and their availability levels is necessary because of the toxic or acute impacts of such invisible pollutants on our entire world or even our possessions. In this work, we present a paradigm based on a hybrid infrastructure consisting of Big Data, the cloud, RESTful APIs, and the Internet of Things (IoT) enabled by the appropriate software and hardware. Air pollution is a major contributor to both the acceleration of global warming and the deterioration of people's health. Our proposed paradigm is crucial in preventing these unfavourable outcomes. The steady increase in the number of cars on the road and the mushrooming of factories are two major sources of pollution. A range of gas sensors and chemical sensors serve as the hardware component of this approach, while the Raspberry platform and its accompanying software architecture are responsible for monitoring and managing pollution data on a web server. Internet of Things (IoT) is a unified framework for enhancing rectification, module efficiency, and monetary gains.