Utilizing microservices, the Internet of Things (IoT), and advanced machine learning algorithms, this study addresses the escalating global demand for sustainable and effective environmental monitoring systems. The focus is on augmenting data gathering, processing, and visualization, with the study delving into the development and deployment of a microservice-based application dedicated to environmental monitoring. Initial data analysis is conducted using simpler models like Logistic Regression. Advanced predictive models, such as XGBoost and AdaBoost, are developed and applied to forecast environmental parameters. Comparative analysis using Random Forest ensures the robustness and accuracy of the chosen models. These models collectively enhance the environmental monitoring system’s capability to provide real-time insights and support decision-making processes. The paper showcases the efficacy of machine learning techniques such as Random Forest, Logistic Regression, AdaBoost, and XGBoost in anticipating and evaluating environmental data. Drawing on a dataset sourced from IoT sensors, the research demonstrates the potential for precise and real-time environmental monitoring. The methodology section provides a comprehensive outline of the project strategy and scientific techniques, accompanied by a thorough review of relevant literature to identify potentials and gaps in the field. The proposed microservice architecture is not only robust and scalable but also adaptable to diverse applications, contributing significantly to the discourse on environmental monitoring. The study’s results suggest promising avenues for further research in this emerging area and lay a solid foundation for enhancing data-driven decision-making in environmental management.