The chapter addresses the urgent scientific and practical challenge of improving environmental monitoring systems in Ukraine, with a particular focus on assessing air quality and radiation levels. The relevance of the research is due to the increasing environmental risks caused by industrial activities, military conflicts, accidents at critical infrastructure sites, and climate change, which threaten public health and sustainable ecosystem functioning. Existing monitoring systems are limited in spatial coverage, have low response efficiency, and lack sufficient transparency for public use, hindering the timely identification of ecological threats. The paper proposes an innovative approach to developing a smart cyber-physical system that integrates stationary and mobile sensors, automated data collection, web and mobile platforms, and Artificial Intelligence (AI) tools, specifically Long Short-Term Memory (LSTM) neural networks, for the prediction of air pollution levels (PM₂.₅) and provide recommendations on radiation hazards. Within the study, simulation models were created to compare the effectiveness of mobile and static sensor systems under various scenarios. The experimental results demonstrate that mobile sensors detected up to 8.5 times more localized contamination points and 4 times more dynamic contamination zones than static systems. In addition, the developed LSTM-based PM₂.₅ forecasting model achieved a forecasting accuracy of approximately 92.2%. The obtained results confirm the effectiveness of the proposed system in enhancing the scalability, adaptability, and analytical capabilities of environmental monitoring, improving early warning mechanisms, and supporting the achievement of key Sustainable Development Goals (SDGs). The study highlights the importance of combining advanced digital technologies and networking solutions with active public engagement to build next-generation environmental protection systems.