This paper proposes an innovative model, Adaptive Intelligent Monitoring for Sustainable Development (AIM-SD), which combines modern communication technologies with machine learning (ML) methods to improve the effectiveness of monitoring environmental, social, and economic processes. A key element of this approach is the integration of adaptive algorithms capable of analyzing data in real-time. This allows for increased forecast accuracy, rapid response to changes, and reduced probability of forecast errors. Monitoring automation plays an important role in this process, reducing the impact of the human factor and increasing system efficiency. AIM-SD uses dynamic regression, Bayesian networks, and adaptive models such as recurrent neural networks (RNN) and long-term memory models (LSTM) to quickly respond to environmental changes and improve forecasts of short- and long-term changes. The problem that AIM-SD solves is associated with a high level of uncertainty and the need to quickly respond to changes in real-time. This involves the integration of data from different sources and allows the creation of a single data collection and processing system for more accurate and faster decision-making. Process automation improves the quality of monitoring and reduces the likelihood of errors, ensuring the efficiency of the system. The new AIM-SD model’s comparison with standard methods shows significant advantages: Its accuracy is 0.885, significantly higher than that of the standard model (0.466), which is based on simple linear regression with high noise levels. This basic approach is less effective in handling data variations, leading to lower accuracy. Additionally, AIM-SD demonstrates superior anomaly resilience (0.969 compared to 0.621 for the standard model), indicating better adaptation to data changes. Unlike the standard model, AIM-SD uses advanced adaptive smoothing techniques, uncertainty correction, and improved anomaly isolation, ensuring more robust responses to fluctuations in input data and a higher degree of accuracy and resilience. The data processing time for both models is almost the same, which confirms the effectiveness of AIM-SD in processing large amounts of information. AIM-SD consistently outperforms Isolation Forest and Bayesian Ridge in accuracy and anomaly resilience, demonstrating superior performance across all sample sizes, especially in noisy and unstable data. Thus, the proposed method, which combines adaptive models and automation of the monitoring process, demonstrates clear advantages over traditional methods, especially in terms of forecasting accuracy and resistance to changes.
