Action Quality Assessment (AQA), a prominent and rapidly growing field in computer vision, focuses on developing automated and objective methods to evaluate the correctness of actions and the level of skill demonstrated in videos. Its diverse applications in sports, healthcare, industrial production, and other emerging domains have attracted significant research attention. Despite remarkable progress, there remains a strong need for a comprehensive and systematic review to consolidate fragmented knowledge and identify future research priorities. In this systematic review, following the standard Kitchenham methodology, 100 relevant studies were selected and analyzed. The field of AQA has evolved from foundational research toward fine-grained, multimodal, generalizable, and multitask approaches. Furthermore, emerging research trends such as continual learning, self-supervised learning, and explainable AI systems—particularly neuro-symbolic approaches—play a pivotal role in providing transparent and actionable feedback. This review offers a holistic perspective on various aspects of the field, including a systematic examination of methods, benchmark datasets, evaluation metrics, existing challenges, and future research directions. Its primary objective is to provide a valuable reference for both newcomers and experienced researchers, facilitating subsequent studies and guiding future advancements in AQA.