To date, there is a limited progress on developing data driven models of constructed wetlands (CWs) for emerging organic contaminants (EOCs) removal; however, there is no process-based model of CWs available that can simulate EOCs. To fill these scientific research gaps, this postdoc research attempts to develop a novel process-based model of CWs by integrating the selected EOCs (pharmaceuticals-PhCs, personal care products-PCPs, steroidal hormones-SHs, and microplastics-MPs) into the existing process-based model (BIO_PORE) and to calibrate and validate the developed process-based model of CWs through lab-scale experimental study. This research will also develop novel data driven predictive models for the removal efficiency of EOCs (PhCs, PCPs, SHs, and MPs) based on conventional water quality parameters in CWs and a novel decision support tool (DST) for the potential application of the developed predictive models. The novel insights, models, and a DST resulting from this research could be instructive for guiding the scientific research, policy development, engineering design, and practical applications in addressing an important challenge of EOCs removal from the wastewater to ensure improved water and environmental quality.