Games have always been a driver for innovation in Artificial Intelligence technology. Evolving neural networks through evolutionary algorithms, also known as neuroevolution, proved to be a powerful and versatile tool in the field of game development. This systematic literature review (SLR) aims to provide a comprehensive overview of recent developments in the implementation of neuroevolution in the context of games. Existing research is categorized in several broad themes such as optimal game-playing, procedural content generation, automated playtesting, behavior imitation and dynamic difficulty adjustment. Implementation techniques including Artificial Neural Networks architecture, fitness function design and evolutionary algorithm choice are investigated with the goal of identifying themes and patterns.