Abstract - This study aims to predict Melasma based on users' data combined with medical practice data community by dermatologists to predict the disease and make some necessary recommendations in the patient screening. This study also helps reduce treatment costs and supports remote patient treatment. In this study, we built a machine learning model to assist dermatologists in predicting a person's risk of Melasma after entering his/her community information. People can use this model through an application to track their risk of Melasma. Combining input community data with the expertise of Melasma specialists, we built a dataset with relevant information to predict Melasma. Based on this dataset, we have statistically described the data characteristics as well as the correlated data parameters that may cause Melasma, then we use the XGBoost algorithm to build a machine learning model to predict whether a person is infected to Melasma or not. The obtained results are going to be applied to assist in predicting whether a person may have Melasma with the input of community information combined with medical practice knowledge about the disease. From this result, it is possible to continue researching and applying artificial intelligence to support diagnosis and treatment of Melasma. Key words: XGBoost algorithm, Melasma disease, machine learning, Melasma prediction.