Early identification of metabolic-associated fatty liver disease (MAFLD) is urgent. Atherogenic index of plasma (AIP) is a reference predictor of obesity-related diseases, but its predictive value for MAFLD remains unclear. No studies have reported whether its combination with waist circumference (WC) and body mass index (BMI) can improve the predictive performance for MAFLD.To systematically explore the relationship between AIP and MAFLD and evaluate its predictive value for MAFLD and to pioneer a novel noninvasive predictive model combining AIP, WC, and BMI while validating its predictive performance for MAFLD.This cross-sectional study consecutively enrolled 864 participants. Multivariate logistic regression analysis and receiver operating characteristic curve were used to evaluate the relationship between AIP and MAFLD and its predictive power for MAFLD. The novel prediction model A-W-B combining AIP, WC, and BMI to predict MAFLD was established, and internal verification was completed by magnetic resonance imaging diagnosis.Subjects with higher AIP exhibited a significantly increased risk of MAFLD, with an odds ratio of 12.420 (6.008-25.675) for AIP after adjusting for various confounding factors. The area under receiver operating characteristic curve of the A-W-B model was 0.833 (0.807-0.858), which was significantly higher than that of AIP, WC, and BMI (all P < 0.05). Subgroup analysis illustrated that the A-W-B model had significantly higher area under receiver operating characteristic curves in female, young and nonobese subgroups (all P < 0.05). The best cutoff values for the A-W-B model to predict MAFLD in males and females were 0.5932 and 0.4105, respectively. Additionally, in the validation set, the area under receiver operating characteristic curve of the A-W-B model to predict MAFLD was 0.862 (0.791-0.916). The A-W-B level was strongly and positively associated with the liver proton density fat fraction (r = 0.630, P < 0.001) and significantly increased with the severity of MAFLD (P < 0.05).AIP was strongly and positively associated with the risk of MAFLD and can be a reference predictor for MAFLD. The novel prediction model A-W-B combining AIP, WC, and BMI can significantly improve the predictive ability of MAFLD and provide better services for clinical prediction and screening of MAFLD.