Postpartum depression (PPD) poses significant risks to both maternal and child health, yet it remains underdiagnosed and undertreated, particularly in developing countries like Bangladesh. Factors such as hormonal changes, lack of communal support and socioeconomic stressors primarily contribute to its prevalence. Despite advancements in research, technological solutions for PPD support are lacking in regions where mental health is deemed negligible. Thus, the objectives of this research are to reveal the underlying factors contributing to PPD among mothers in developing countries and to propose a novel application based on these factors for PPD detection. A Design Science Research (DSR) approach is adopted to achieve these objectives. In this research, firstly 17 factors that could serve as effective indicators for detecting PPD were identified through interviews with 12 key informants, including doctors and patients. Next, for each identified factor, visual and scenario-based questionnaires were designed to facilitate effective screening through user feedback. Then, an Android application, 'PPD Screening App', was developed, which provides users with visual, auditory, and multilingual (in English and Bengali) scenario-based questionnaires for each identified factor, as well as the questionnaires proposed in the Edinburgh Postnatal Depression Scale (EPDS) for PPD detection. Finally, the developed application was evaluated with 45 new mothers in the postpartum period and demonstrated high accuracy (96%) compared to the traditional screening method (84%). The effectiveness of the application was also explored in relation to participants' demographic profiles (age and IT literacy), and participants provided very positive feedback on the usability of the application. This research thus contributes in improving early detection and intervention for PPD, ultimately enhancing maternal and child well-being.