Background: Environmental pollution is a major global public health concern, contributing significantly to cardiovascular, respiratory, neurological, and metabolic disorders. Although epidemiological studies have long established associations between pollutant exposure and adverse health outcomes, traditional approaches often rely on late-stage clinical endpoints. Objective: This review aims to synthesize current evidence on the impact of environmental pollution on human health, emphasizing the role of imaging biomarkers and their integration with epidemiological modeling to enhance risk assessment and precision environmental health. Methods: A narrative multidisciplinary review was conducted using literature retrieved from PubMed, Scopus, and Google Scholar. Peer-reviewed studies focusing on environmental exposures, imaging biomarkers (MRI, CT, PET, and ultrasound), and epidemiological modeling were included. Data were extracted on exposure type, imaging modality, biological outcomes, and analytical approaches. Findings were narratively synthesized and organized by organ system and methodological framework. Results: A total of 38 relevant studies were analyzed. Air pollution, particularly PM2.5, emerged as the most studied exposure, showing consistent associations with increased coronary plaque burden, arterial inflammation, reduced brain white matter integrity, and pulmonary remodeling. Imaging biomarkers demonstrated early structural and functional alterations preceding clinical disease. Integration of imaging features into epidemiological models improved disease prediction accuracy by approximately 20–35%. Mediation analyses indicated that imaging-detected inflammation accounted for 30–45% of pollution-related cardiovascular risk. Machine learning approaches further enhanced individualized risk stratification. Conclusion: Environmental pollution induces measurable biological changes across multiple organ systems that can be detected using advanced imaging techniques. The integration of imaging biomarkers with epidemiological models strengthens causal inference, improves predictive accuracy, and supports early intervention strategies.