Air quality is a key determinant of public health, increasingly impacted by natural events such as wildfires and volcanic eruptions, as well as human-induced sources like vehicular and industrial emissions. This research focuses on predicting air pollution levels using two advanced models: Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA). A dataset is developed by combining precise ground station data from multiple ground stations. It also features real-time inputs from low-cost ESP32-powered IoT sensors. The sensor fusion approach enhances both spatial coverage and data granularity, offering a more comprehensive representation of environmental conditions. The ThingSpeak IoT platform is employed for live data collection, visualization, and remote monitoring. LSTM networks are selected for their capability to model complex, long-term dependencies in time-series data, while ARIMA provides a robust statistical baseline for comparison. Experimental results demonstrate that models trained on the fused dataset significantly outperform those using individual data sources, resulting in more accurate and reliable air quality forecasts. This study presents a scalable and cost-effective framework for air quality monitoring, supporting timely, data-driven interventions by environmental agencies and contributing to improved public health management in both urban and rural areas.