Air pollution poses a significant threat to public health, particularly in urban environments of developing nations where monitoring infrastructure is often sparse or outdated. This thesis presents the design and deployment of a low-cost, real-time air quality monitoring system for Colombo, Sri Lanka. Utilizing a suite of low-cost sensors ranging from particulate matter detectors (SDS011) to gas sensors (MQ and MiCS series), GPS modules, and environmental sensors (DHT22, BMP180) the system captures a wide array of environmental parameters including PM2.5, PM10, CO, NO₂, SO₂, temperature, humidity, and pressure. Data is collected via Raspberry Pi-based architecture and uploaded to Google BigQuery, where it undergoes statistical preprocessing and machine learning-based anomaly detection using Autoencoders, Gaussian Mixture Models, and Isolation Forests. Visualizations are delivered through interactive dashboards in Looker Studio, providing public access and decision-making support for authorities. The proposed system aims to bridge the gap between data scarcity and high spatial resolution needs in urban air monitoring. The results indicate that, when calibrated and combined with intelligent preprocessing, low-cost sensors can offer reliable environmental insights, thus supporting data-driven governance and community health awareness. The project adheres to strict ethical, legal, and data security standards to ensure responsible deployment and use.