Real-time air quality monitoring and predictive pollution control are critical for addressing escalating environmental and public health challenges, particularly in low-income areas with limited infrastructure. This paper explores the integration of big data analytics and IoT to develop cost-effective, scalable solutions for real-time air quality assessment. The proposed framework aims to identify pollution patterns, predict air quality trends, and provide actionable insights for policymakers. A unique feature of this study is its emphasis on low-cost sensor deployment and edge-computing techniques to ensure accessibility in resource-constrained settings. The interdisciplinary approach combines environmental science, AI, and public health perspectives to establish a holistic framework for data collection, analysis, and decision-making. Additionally, this paper addresses the integration of findings into policy frameworks by proposing data-driven recommendations for urban planning, industrial regulation, and community health interventions. The results demonstrate significant advancements in predictive accuracy and actionable intelligence generation while minimizing implementation costs.