Air pollution has become one of the most serious environmental and public health concerns globally, especially in rapidly urbanizing regions. Conventional air quality monitoring systems primarily provide real-time pollutant measurements but lack predictive capabilities that are essential for proactive decision-making. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies have enabled the development of intelligent systems capable of forecasting air pollution levels with high accuracy. This paper presents a comprehensive review of AI-based air pollution prediction techniques, including traditional machine learning models, deep learning architectures, hybrid approaches, and emerging transformer-based methods. The study critically analyzes existing research, identifies key limitations, and highlights research gaps in current systems. Furthermore, an intelligent framework named AirVibe AI is proposed, which integrates real-time data acquisition, predictive analytics, interactive visualization, and user-centric features such as personalized alerts and AI chatbot support. The paper aims to provide a structured understanding of modern air quality prediction systems and contribute toward the development of scalable, accurate, and user-friendly solutions for environmental monitoring and smart city applications.

Air pollution has emerged as a critical environmental and public health issue, particularly in rapidly urbanizing and industrialized regions. Conventional air quality monitoring systems are primarily limited to real-time observation and lack the capability to forecast future pollution trends. This limitation restricts their effectiveness in enabling proactive measures and timely interventions. With the rapid advancement of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT), there has been a significant shift toward intelligent air quality prediction systems that can analyze large-scale environmental data and provide accurate forecasts. This paper presented a comprehensive review of existing air pollution prediction techniques, including traditional machine learning models, deep learning approaches, hybrid architectures, and transformer-based methods. Machine learning models such as Random Forest and Support Vector Machines are efficient and easy to implement but are limited in handling temporal dependencies. Deep learning models, particularly LSTM and CNN, offer improved accuracy by capturing complex temporal and spatial patterns, although they require large datasets and higher computational resources. Hybrid models have demonstrated superior performance by combining the advantages of both approaches, making them more robust and reliable for real-world applications. In addition to reviewing existing methodologies, this study identified several critical research gaps. These include the lack of real-time prediction capabilities, issues related to data quality and missing values, insufficient integration of meteorological parameters, and limited user-centric features such as personalized alerts and intuitive visualization. Furthermore, challenges such as high computational requirements and limited model interpretability continue to hinder the widespread adoption of advanced prediction systems. To address these limitations, the proposed AirVibe AI framework integrates AI, ML, and IoT technologies into a unified system that supports real-time monitoring, accurate prediction, and user-friendly interaction. The system leverages multiple data sources, including IoT sensors and environmental APIs, and employs advanced machine learning and deep learning models to generate reliable AQI forecasts. Additionally, features such as interactive dashboards, location-based alerts, and AI chatbot assistance enhance user engagement and accessibility. The findings of this study highlight that AI-driven air pollution prediction systems have strong potential to transform environmental monitoring and management. By providing early warnings and actionable insights, such systems can help reduce health risks, support government policies, and promote sustainable urban development. In conclusion, the integration of AI and IoT in air quality prediction represents a promising direction for future research and practical implementation. Future work should focus on improving model efficiency, enabling real-time deployment, incorporating explainable AI techniques for better transparency, and expanding the system to large-scale smart city applications. Such advancements will contribute significantly to building cleaner, healthier, and more sustainable environments.

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