<![CDATA[General Background: Environmental pollution from heavy metals and organic contaminants has escalated globally, generating complex biological disruptions across molecular, cellular, and ecological levels. Specific Background: Advances in high-throughput sequencing and multi-omics technologies have revealed that pollutants induce genomic and epigenetic alterations, yet interpreting these multidimensional datasets remains challenging. Knowledge Gap: Existing studies lack integrated analytical frameworks capable of linking pollutant exposure with genome-wide molecular responses in a precise and predictive manner. Aims: This review synthesizes current evidence on pollutant-induced molecular changes and evaluates the potential of artificial intelligence (AI) to model and predict biological responses. Results: Recent applications of machine learning—such as Random Forests, Support Vector Machines, and Convolutional Neural Networks—demonstrate strong performance in identifying key genetic markers, forecasting epigenetic modifications, and estimating organismal vulnerability before clinical symptoms appear. Novelty: This article highlights the emerging role of AI-driven genomic modeling as a transformative approach that integrates environmental and genomic datasets to capture multilevel biological responses with high accuracy. Implications: The integration of AI with genomics offers a proactive strategy for early detection of pollution-induced molecular changes, enhances environmental risk assessment, and informs targeted remediation, biodiversity protection, and long-term ecosystem management.Highlight : Environmental pollutants cause DNA methylation changes, histone modifications, and disruptions in cellular defense mechanisms. Machine learning algorithms predict epigenetic changes and identify genetic markers before physiological symptoms manifest. AI integration enables early pollution detection, bioremediation support, and proactive ecosystem management. Keywords : Environmental Pollution, Artificial Intelligence, Environmental Genomics, Epigenetic Change, Predictive Modeling]]>
