Conversation serves as a crucial means for individuals to express their intentions, feelings, attitudes, and personalities, making natural conversational interaction a vital technical competency for conversational agents. With the growing advancements in deep learning and natural language processing techniques, these systems have gained significant traction across various sectors. However, a key challenge lies in developing chatbots that can generate human-like replies adaptable to users’ personalities. This article presents a comprehensive review of previous research on personality adaptive chatbots. By employing a systematic review approach and analyzing 3524 publications from recognized digital databases, we identify 66 relevant works focused on personality adaptive chatbots. The review examines recent studies on personality interpretations and definitions, providing valuable insights into different perspectives on personality. Additionally, it categorizes and analyzes various deep learning approaches used in personality recognition, identifying effective techniques for capturing and understanding a users’ personalities. Furthermore, the review explores the consideration of personal characteristics such as linguistic style, behavior traits, and Persona in adapting chatbot responses to users’ preferences. Lastly, through an analysis of relevant chatbot implementations, the review identifies trends in incorporating personality adaptation and metrics to assess effectiveness and user satisfaction. By synthesizing these findings, we contribute to a deeper understanding of personality adaptive chatbots and the challenges and opportunities associated with developing chatbots that can generate human-like replies adaptable to users’ personalities.