Environmental monitoring information systems have advanced in data collection and analysis, utilizing methods like remote sensing, mathematical modeling, and machine learning; however, gaps remain in automating data transformation, resolving semantic conflicts, integrating qualitative knowledge, and leveraging advanced AI, including Large Language Models (LLMs), for reporting. This study aimed to address these gaps by developing conceptual models for the processes of data collection, forecasting, and preparation for expert analysis within these systems, integrating mathematical/statistical analysis, machine learning, and LLMs to automate report generation. The methodology involved analyzing existing approaches and formulating conceptual frameworks, including the development of tuple-based models to represent relationships between ecological objects, monitoring data, AI models, forecasts, and recommendations, alongside the proposed integration of mathematical/statistical methods (e.g., ARIMA, SVM), machine learning for prediction and anomaly detection, and the adaptation of LLMs using Retrieval-Augmented Generation (RAG) for enhanced reporting. The primary results are a suite of conceptual models detailing an integrated system that incorporates these diverse analytical techniques, specifically outlining the use of LLMs with RAG for automated reporting from structured data and employing tuple models to formally represent system interconnections; mathematical formulations for key processes were also outlined. These developed conceptual models provide a foundation for creating more efficient and integrated environmental monitoring systems, enhancing data processing, improving forecasting accuracy, leveraging advanced AI techniques for automated report generation, and ultimately supporting more informed environmental management decisions essential for lean, circular, green, and sustainable supply chains and production.