To apply machine learning models on clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and to create an algorithm to identify these patients without bile acid measurements. This retrospective study included 336 women with low-risk pregnancies and a chief complaint of pruritis without rash during the second and third trimesters. Data extracted included: (1) Demographic: age, BMI, ethnicity; (2) Obstetric: gravidity, parity, gestational week, singleton/multiple pregnancy; (3) Clinical: Season, duration and location of pruritus, previous ICP; (4) Laboratory: GOT, GPT levels, bile acid levels. The primary outcome was an elevated bile acid measurement ≥10 μmol/L, regardless of liver enzyme levels (GOT/GPT). We used different machine learning models and statistical regression to predict elevated bile acid levels. Among 336 women who complained about pruritis, 167 had bile acids ≥10 μmol/L and 169 had normal bile acid levels. Women with elevated bile acids tended to be older than those with normal levels (33.1±6.1 vs. 30.3±5.3 years), with higher parity (p=0.001) and higher GOT (77±79 vs. 29±25, p=0.001) and GPT levels (115±140, vs. 32±43, p=0.001). Using machine learning models, the XGB Classifier model was the most accurate (AUC, 0.9) followed by the K-neighbors model (AUC, 0.86); and then the SVC model (AUC, 0.81). The model with the lowest predicative ability was the logistic regression (AUC, 0.76). The maximum sensitivity of the XGB model was 86% and specificity 75%. The best predictive parameters of the XGB model were elevated GOT (Importance 0.17), elevated GPT (Importance 0.16), family history of bile disease (0.16) and previous pregnancy with ICP (0.13). Machine learning models using clinical data may predict ICP more accurately than logistic regression does. Using detection algorithms derived from these techniques may improve identification of ICP, especially when bile acid testing is not available.