The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations.

An Internet of Things (IoT)-like application is an advanced system for automation and data analytics that leverages communication networks, sensors, and artificial intelligence to provide a comprehensive solution for a product or service. The development of IoT technologies has facilitated advancements in automated systems across various industries like healthcare, agriculture, smart cities, smart homes, and transportation. This paper presents an IoT-based system designed for the real-time monitoring and management of a milk dispenser. The system integrates an ESP32 microcontroller, sensors for temperature and milk level measurement, and cloud storage for data analysis and visualization. By using machine learning algorithms, the system predicts milk consumption trends and detects potential dispenser blockages, enhancing operational efficiency. Experimental evaluation showed that the Random Forest and Gradient Boosting regression models achieved high predictive accuracy, with the best configurations reaching a coefficient of determination (R2) of 0.95, a Mean Absolute Error (MAE) as low as 1.05 liters, and a Root Mean Squared Error (RMSE) of 1.32 liters, confirming the strong reliability of the proposed approach.

Real-time environmental monitoring is increasingly supported by Internet of Things (IoT)-based sensing infrastructures and machine learning techniques capable of processing high-frequency urban environmental data. However, many existing solutions remain focused primarily on data acquisition, visualization, or isolated prediction tasks, with limited integration of predictive analytics and human expertise within a unified decision-support framework. This limitation highlights the need for intelligent monitoring approaches that combine continuous sensing, short-term prediction, and Human-in-the-Loop interpretation. Hybrid Human–AI Collaborative Networks (HCNs) are increasingly relevant for supporting environmental monitoring and decision-making in smart cities. This paper proposes an Artificial Intelligence of Things (AIoT)-enabled collaborative framework that integrates distributed uRADMonitor sensors, cloud-based data management, machine learning models, and human stakeholders into a unified monitoring ecosystem. A distributed uRADMonitor sensing network was deployed across multiple locations in Sibiu for continuous environmental monitoring, while the machine learning experiments presented in this study were conducted independently using three sensing-node datasets collected from different neighbourhoods in Sibiu. The same overall modelling and validation methodology was applied to each dataset, while the predictor set reflected the environmental variables available at each sensing node. Several machine learning algorithms were evaluated for Air Quality Index (AQI) prediction, including Linear Regression, Random Forest, and Gradient Boosting. Under a random train–test split, Random Forest was the best-performing contemporaneous model across all three sensing nodes, although predictive performance varied substantially between locations. In contrast, the short-term forecasting experiment provided the operationally relevant predictive component, with Ridge Regression achieving positive skill relative to persistence across forecasting horizons from 5 to 120 min.