Air quality significantly impacts human health and economic conditions, making precise and timely assessment crucial in urban areas. Existing studies often fail to predict pollution accurately in smaller areas due to varying meteorological conditions. Most existing prototypes rely on standard ML models, trained with generic, publicly available datasets, which often lack local dynamics, adaptability, and synchronization. To address these challenges, in this paper, we propose a new Hybrid Edge-Cloud Air Quality Monitoring System. For initial model training a robust testbed is created by Data Fusion of air quality data from diverse sources like Application Programming Interfaces and public datasets. The system trains and refines ML models in the cloud, identifies key features to reduce feature dimensions, and leverages local decision-making using site-specific data collected by sensors on edge devices. Sensors monitor local environmental parameters for real-time analysis and decision-making, ensuring accurate predictions for specific urban locations while optimizing performance and minimizing resource usage on edge devices. Explainable AI techniques improve transparency and confidence in decisions. The model is validated by simulating various ML models on the testbed, with and without feature extraction.