Emerging pollutants such as pharmaceuticals, industrial chemicals, heavy metals, and microplastics are a growing ecological risk affecting water and soil resources. Another challenge in current wastewater treatments includes tracking and treating these pollutants, which can be costly. As a growing concern, emerging pollutants do not contain lower limit levels and can be detrimental to aquatic resources in minuscule amounts. Thus, the assessment of multiple emerging water pollutants in community-based water sources such as surface water and groundwater is a prioritized area of study for water resource management. It provides a basis for ecological health management of arising diseases such as cancer and dengue caused by unsafe water sources. Accordingly, by utilizing artificial intelligence, wide-range and data-driven insights can be synthesized to assist water resource management and propose solution pathways without the need for exhaustive experimentation. This systematic review examines artificial intelligence-assisted modelling water resource management for emerging water pollutants, notably machine learning and deep learning models, with proximity dependence and correlated synergistic health effects to both humans and aquatic life. This study underscores the increasing accumulation of these emerging pollutants and their toxicological effects on the community, and how data-driven modelling can be utilized to assist in research gaps related to water treatment methods for these pollutants.