This study systematically investigated the association between triclosan (TCS) exposure and Alzheimer's disease (AD) risk via integrated bioinformatics approaches. TCS-AD-related genes were identified using bioinformatics tools and public databases, followed by the screening of key genes through multi-model machine learning algorithms (LASSO, SVM-RFE, RF) to mitigate random errors in small sample sizes. DRD2 was confirmed as the most robust core gene by LASSO confidence interval analysis and SHAP evaluation, while APP and SLC6A3 were validated through cross-method intersection. Findings from functional enrichment analysis, Mendelian randomization, and molecular docking demonstrated that TCS may affect AD pathogenesis through these key genes. Moreover, a stable AD risk scoring model and predictive formula were developed via logistic regression analysis of T-A-Key Genes. This study constitutes an innovative and transformative research endeavor. Building upon the understanding of TCS-induced neurotoxicity, it extends to the clinical translational direction of predicting AD onset risk, thereby establishing a quantitative association model of "environmental exposure-core genes-disease risk. " It not only provides novel evidence for the potential role of TCS neurotoxicity as an environmental pathogenic factor in AD, emphasizing the necessity of prioritizing risk management of daily chemical exposure in AD prevention and treatment, but also offers a scientific foundation for the formulation of public health policies aimed at preventing long-term TCS exposure.