Postpartum depression (PPD) is a major threat to maternal mental health globally and has a high incidence in new mothers. Current routine screening mainly depends on subjective questionnaires, which unavoidably results in under diagnosis and late intervention of PPD. The rapid development of machine learning (ML) and artificial intelligence (AI) technology brings an unprecedented opportunity for PPD detection by automatically analyzing rich data sources including social media, electronic health records (EHRs), and physiological signals. This abstract reviews current research on ML and AI applications in PPD detection, highlighting methodologies, challenges, and potential benefits of integrating advanced technologies into clinical practice. By leveraging computational techniques for early identification and personalized intervention, healthcare systems can potentially mitigate the impact of PPD on maternal well-being and improve outcomes for mothers and infants alike. In this work we have used (15,039) structured data having 980 postpartum depressions and 523 normal samples. Nine classifiers including Decision tree, LGBM, Random forest classifier, catboost classifier, SVM, Adaboost, Gradient classifier and soft voting classifier. Among the all classification accuracies, decision tree, RFC, LGBM, CATBOOST Classifiers has outperformed by giving 99% accuracy as well as AUC score too. Modelling done with ensemble classifiers to predict postpartum depression can be used in future studies.