<![CDATA[The rapid escalation of environmental challenges, including air pollution, climate change, water contamination, and noise pollution, has necessitated the development of advanced monitoring and control systems. Traditional environmental monitoring approaches, which rely on manual data collection and delayed analysis, are no longer sufficient to address dynamic and complex environmental conditions. This research presents a comprehensive framework for IoT-driven smart environmental monitoring systems integrated with artificial intelligence (AI) and adaptive control mechanisms. The study evaluates the contribution of different sensor categories in environmental monitoring systems, revealing that air quality sensors account for 32%, temperature sensors 24%, humidity sensors 18%, water quality sensors 14%, and acoustic sensors 12%. Additionally, the performance improvements achieved through intelligent systems are analyzed, showing enhancements in predictive accuracy (36%), anomaly detection (34%), response efficiency (31%), energy optimization (29%), and system adaptability (27%). The proposed framework is based on a multi-layered architecture that integrates sensing, communication, processing, and application layers. Advanced machine learning models are employed to analyze environmental data, detect anomalies, and generate predictive insights. The integration of edge and cloud computing further enhances system efficiency by reducing latency and improving real-time responsiveness. The findings demonstrate that IoT and AI-driven environmental monitoring systems significantly improve detection capabilities, data accuracy, and decision-making processes. These systems enable proactive environmental management, reduce risks, and support sustainability initiatives. The research contributes to the development of intelligent environmental infrastructures and highlights the potential of smart monitoring systems in addressing global environmental challenges.]]>
