Artificial Life models and algorithms are informed by natural and biological processes and phenomena. Artificial Life finds particular use in simulating large, complex systems such as large scale ecosystems or social networks, where the interaction between system entities may give rise to emergent behaviours. Despite the increasing popularity and ubiquitous nature of complex systems, the extent of which artificial life approaches are considered in complex systems modelling and their application across complex systems domains is still unclear. To better understand the overlap between artificial life and complex systems, we conducted a systematic literature review of last decade’s artificial life research that had a complex system focus. We performed an automated search of all relevant databases and identified 538 initial papers, with 194 in the candidate set, resulting in 115 primary studies. Our results show that the three most frequent application domains are simulation, followed by social modelling, and biological modelling. We find a plethora of paradigms that can be broadly classified into three main categories, namely, biological, social, and hybrid. We identify the artificial life paradigms that are used to generate the most common complex systems properties as well as a number of research challenges that are critical for the growth of both artificial life and complex systems modelling.