This study aims to develop the models for predicting the retail pricing of jewelry by using data source from online retail diamond ring stores. There are 2,206 records of ring data and 187,821 records of loose diamond data. This study develops and compare a performance of three models consist of Multiple Linear Regression (MLR), Random Forest (RF), and Deep Neural Network (DNN). The evaluation metrics used for comparing algorithms are accuracy of prediction using MAE and MAPE. The results show that MAE for the ring price prediction of MLR, RF, and DNN are $688.36, $235.33, and $273.00, respectively. In addition, MAE for a diamond price prediction of MLR, RF, and DNN are $3254.03, $450.44, $445.94, respectively. The re-sults show that RF and DNN give higher accuracy rate than MLR. However, the accuracy rate of RF and DNN are slightly different.