Decoding Human Exposure to Toxic Metals: Linking Environmental Distribution to Health Outcomes Through Multivariate Analysis

Environmental contamination by trace and heavy metals (THMs), driven by anthropogenic activities like industry, mining, and agriculture, poses a persistent global public health threat. These nondegradable contaminants lead to widespread human exposure via ingestion, inhalation, and dermal contact, causing diverse adverse health outcomes, including neurodevelopmental, cardiovascular, cancerous, renal, and endocrine disorders. Assessing risks from complex real‐world metal mixtures is challenging. This review critically synthesizes current knowledge on THMs’ sources, distribution, exposure pathways, and health effects. It emphasizes the indispensable role of multivariate data analysis (MVDA), including principal component analysis, cluster analysis, source apportionment models, and machine learning and in silico approaches in deciphering these complex exposure‐health relationships. We evaluate how these tools help identify contamination sources, interpret biomonitoring data, and assess the impacts of metal mixtures. Current monitoring strategies, biomarker discovery, and preventive measures (regulatory, technological, and public health) are discussed. Despite progress, significant research gaps persist concerning low‐dose mixture toxicology, subclinical effects in vulnerable populations, and the predictive power of multiomics and in silico models. Addressing these requires sustained transdisciplinary collaboration and innovation. This review highlights the necessity of a holistic, science‐driven strategy to manage THM pollution and protect global health.