The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT’s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience.

This paper introduces SmartBuildSim, an open-source synthetic-twin framework that generates configurable and reproducible multi-sensor building streams using lightweight statistical models with tunable trend, seasonality, correlation, delays, and anomaly mechanisms. Deterministic seeding ensures experiment-level reproducibility, while modular pipelines support unified evaluation across forecasting, anomaly detection, and RL tasks. A comprehensive validation against an ASHRAE Great Energy Predictor III reference signal demonstrates that the synthetic data capture realistic magnitude and variability (KS ≈ 0.32; DTW ≈ 9.69), while preserving interpretable and controllable temporal structure. Benchmark results show that simple linear models achieve strong forecasting performance (RMSE ≈ 21.27), IsolationForest reliably outperforms LOF in anomaly detection (F1 ≈ 0.17 vs. 0.10), and Soft-Q Learning provides substantially more stable RL convergence than tabular Q-learning (variance reduced by >95%). Scenario-level analyses further illustrate reproducible daily cycles, zone-specific differences, and the scalability of model behaviour across building configurations. By combining declarative YAML configurations, deterministic randomness management, and an extensible scenario engine, SmartBuildSim provides a transparent and lightweight alternative to high-fidelity building simulators. The framework offers a practical, reproducible testbed for smart-building AI research, bridging the gap between simplistic synthetic datasets and complex physical digital twins. All code, tables, figures, and a Google Colab workflow are openly available to ensure full replicability.