A Predictive Ai Framework For Proactive Pollution Control And Environmental Protection

Abstract

Background of the Study

Environmental pollution has become  one of the most  dominant global concerns due to its profound as well as  far-reaching impact on the  human health, biodiversity, andalso the  climate systems. Factors such as rapid urbanization, exponential population growth, industrial emissions, vehicular exhaust, as well as the  unsustainable agricultural practices have  mainly escalated air, water, and soil pollution levels worldwide (Abbaspour  et al., 2021). Despite diverse country wide and global rules aiming to reveal and reduce pollutants, many present day structures perform in a reactive mode—intervening only after essential environmental thresholds have been breached. This reactive model is inadequate within the face of dynamic environmental adjustments, where early detection and mitigation are key to minimizing harm.In parallel, technological improvements—mainly in records technological know-how and Artificial Intelligence (AI)—have opened new frontiers for proactive and preventive environmental management. Machine Learning (ML) and Deep Learning (DL) models can perceive non-linear styles in environmental records, detect pollution resources, and forecast pollution stages with excessive accuracy. These abilities create opportunities for real-time choice-making and focused interventions, moving pollution control from a passive to an anticipatory paradigm.