Sewage systems are critical urban infrastructure elements, essential for flood prevention, environmental protection, and public health. As part of sewage system infrastructure, and similar to other infrastructure, sewer systems are deteriorating due to aging. Current maintenance measures for sewer systems predominantly rely on manual processes based on heterogeneous and inconsistent data. However, digital sewer models are expected to facilitate proactive maintenance strategies to mitigate damage and extend the lifespan of sewer systems. This paper investigates current state-of-the-art research concerning the implementation of digital twins in sewer system maintenance during the operating phase through a systematic literature review (SLR). The findings indicate that digital twins offer numerous advantages for proactive maintenance in sewer systems, specifically for sewer system assessment and damage prognosis. Nonetheless, in the current body of research, confusion between digital twins and simpler digital shadows and digital models is apparent. Furthermore, the implementation of digital twins for sewer system maintenance is associated with several challenges, including data-related, technological, and methodological challenges. Finally, the outcome of the SLR sheds light on potential research directions towards realizing digital twins based on building information modeling (BIM), e.g., BIM-based digital twins, for sewer system maintenance.