Harmful emission of pollutants causes air pollution. It affects the human population as well as the environment. Further it affects the ecosystem components. It becomes of utmost necessity to address this issue. This enforces prognostication of the pollution levels in air using machine learning (ML) techniques. Thus the expedition for predictive impact in monitoring of air quality using ML techniques has popped up as a crucial area of research. The presented work here laid a test case of a real-time case study of Hyderabad industries inclusive of industrial, rural, and residential areas. The exit gases from the industries are categorized based on Air Quality Index (AQI). Random Forest (RF), K-Nearest Neighbours (KNN), and Decision Tree (DT) and are some of the proposed method for predicting the AQI for health care. A demonstrative analysis for comparison of ML techniques has provided a staley rate in accuracy rate for predicting air quality. This enhancement in health care thus clearly poses a way in achieving sustainable development goal-3(SDG-3).