Human–Machine Communication (HMC) explores the joint meaning-making processes between humans and communicative intelligent machines, as well as its broader societal implications. The rapid advancements in Natural Language Processing (NLP) and Natural Language Generation (NLG) have transformed intelligent machines from passive tools to active social actors, capable of understanding and generating human-like messages. This paper evaluates the evolving research agenda of HMC, focusing on popular communication frameworks such as the CASA paradigm, the dual-process model, and social presence. It examines their theoretical foundations, transformations, limitations, and potential future extensions. Furthermore, this paper discusses the societal and ethical dimensions of HMC and provides recommendations for advancing research, including interdisciplinary approaches and the refinement of methodologies.