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Indoor air pollution is more dangerous for residents. So, it is necessary to monitor the quality of indoor air and take some preventive steps to reduce the possible dangers to the health of the inhabitants. The cost and maintenance factors of air quality (AQI) systems lead the researchers to model, design, and implement low-cost indoor AQI monitoring systems. In this research, we proposed an indoor AQI monitoring system with a data-driven model to predict the AQI through the Neural Network Algorithm and Block-chain. The Internet of Things (IoT) connects and processes data, and low-cost sensors collect the data from the environment. The Indoor Air Quality system consists of temperature, humidity, Carbon Di Oxide, Particulate Matter, Carbon Mono Oxide, and LPG. The data are collected from five different sensors, and the NN decision-making model is used to predict the AQI to prevent harmful situations. The suggested IoT-based smart block-chain technology plays a vital role by imparting scalability, privacy, and reliability. This study will work effectively with ease of use, cost-effectiveness, and maintenance of the entire system. Taif University - grant No. TURSP-2020/55.