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One challenge of Augmented Reality applications is to identify the context and semantics of the real environment. Studies on object and action recognition have evolved from improvements in machine learning (ML) techniques, allowing them to be annotated and recognized. This study aims to characterize the current knowledge about the use of machine learning to recognize objects and actions in Augmented Reality environments in order to increase context context awareness. Based on a systematic literature review (SLR) of the papers published on the topic between 2017 and 2021 in the Scopus, IEEE Xplore, and Web of Science, nine studies were selected. The results indicate that, on the one hand, there is great demand to be explored in industry, entertainment, education, healthcare, among other other application domains. However, these real-time interactive systems technologies still have challenges to be solved, involving to be solved, involving network communication and limitations in object and action recognition and action recognition model for broad context and in prediction time. It is concluded that further research is is needed to investigate how Augmented Reality can work in object and action recognition to object and action recognition to increase context awareness.