Effective air quality monitoring is crucial for understanding and mitigating the adverse impacts of pollution. This research confronts the challenges of obtaining precise data and identifying sources of pollution. It achieves this by introducing an effective algorithm for the deployment of air quality monitoring devices (AQMDs), thereby enhancing the efficiency of their distribution. Our study initiates by verifying current setups of AQMD, which have a limited number of pre-installed devices. This algorithm considers the spatial distribution of pollution sources to minimize the capital and operational costs associated with AQMD installation. It utilizes a dataset that spans 11 months and covers 759.13 km² of Delhi and 75 km² of Durgapur. The proposed algorithm demonstrates its efficacy by achieving an accuracy rate of 90% − 95% in predicting air quality. By strategically selecting monitoring locations based on the distribution of pre- installed AQMDs and existing pollution sources, the algorithm significantly reduces unnecessary costs while maximizing data coverage for comprehensive testing and analysis. This research contributes to optimizing air quality monitoring networks, facilitating better decision-making for pollution control and resource allocation. The outcomes bear significance for urban planners, policymakers, and environmental researchers who are in search of cost-effective solutions to address air pollution challenges in affected areas.