Timely detection of hazards, management resources, and informed policy development all require effective environmental monitoring. However, the traditional UAV-based model and the single-sensor solution suffer from low adaptability, low detection accuracy under complex conditions, and difficulties to expand, merge and efficiently operate the monitoring data. In a response to this call, we describe an intelligent drone-based environmental monitoring system, considering and addressing the aforementioned limitations, by utilizing adaptive multisensor fusion. The proposed real time platform will feature a dynamic integration of disparate sensors (optical, infrared, environment modules) into a high-level mission driven sensor fusion engine guided by high level mission goals and environmental feedback. On-board intelligent data analytics achieve real-time anomaly detection and data processing, as well as sensor optimization, greatly enhancing reliability of detection under different geographical and climate conditions. By means of edge and IoT-connected networking, elliptic connect and seamlessly integrated with off-site databases and decision-making system for mass real-time environment monitoring. A modular and energy efficient design make the proposed architectural solution scalable and cost-effective in operation, this in response to complaints of endurance, latency and regulation found in existing literature. Experimental results and case studies show that the proposed system has better accuracy, stability and operational flexibility than existing UAV-based monitoring methods, and has set a new benchmark in full-scale, high precision environmental monitorin