This study determines whether 9 machine learning (ML) models can reliably predict depression risk based on polyfluoroalkyl substance (PFAS) exposure using National Health and Nutrition Examination Survey (NHANES) data. PFAS are synthetic chemicals frequently present in commercial products. Researchers utilized data from seven NHANES cycles between 2005-2018, and analyzed 9,074 adults with both PFAS panels and depression screening using PHQ-9, with a score of 10 or higher being the classification for depression, along with covariates like age, gender, race, and education levels being accounted for. Researchers identified 7 different PFAS by online SPE-HPLC-TIS-MS/MS (a type of spectrometry), and trained nine different ML models, evaluating their performance based on receiver operating characteristic curve (ROC), AUC, accuracy, sensitivity, specificity, and interpretability using partial dependence analysis and SHAP; depression risk prediction web calculator was developed with Gradio framework and statistical analyses were performed. CatBoost performed the best with an AUC of 0.75 with 73% accuracy), and CatBoost ranked poverty-to-income ratio (PIR), age, smoke, BMI, marital status, and perfluorooctane sulfonic acid (PFOS), among the top contributors to depression. PFOS was the most highly ranked PFAS. Partial dependence plots (PDPs) indicated a threshold effect for PFOS at 11.66 ng/mL; beyond this threshold, higher PFOS were associated with reduced depression risk contributions. This study demonstrates the possibilities of an ML framework linking PFAS profiles, especially PFOS, to depression risk patterns.