The growing urbanization in India has been closely associated with the expansion of the cement industry. Acknowledging the significant impact of air pollution from the cement industry on the environment, human health, and the economies of affected nations is imperative. To safeguard individuals from health issues arising from air pollution, it is crucial to accurately and promptly anticipate the ambient air quality index. The pollution control board conducts an annual audit to ensure the implementation of Sustainable Development Goals (SDG) policies aimed at climate mitigation. A low-cost sensor employing a cloud-edge architecture was installed in the cement industry to monitor Ambient Air Quality for ubiquitous computing. The innovative approach in this study involves monitoring air quality by detecting anomalies during a calibration drive. Comprehensive emissions monitoring is achieved across various operational scenarios by combining data from multiple sensors, including pollution sensors, PPM sensor arrays, and environmental sensors. The results demonstrate a substantial enhancement in the accuracy of air quality monitoring and the early detection of potential health risks. The article elucidates anomaly detection algorithms, namely Isolation Forest (IFor), Local Outlier Factor (LOF), and DBSCAN, compared between the industry-set sensor sn and the environment lab-tested referenced sensor sv. After removing anomalous readings, RMSE scores of 22.75 and 17.7 were recorded. The article emphasizes adaptable solutions for avoiding unhealthy air, benefiting the environment, and improving public health.