This paper presents a systematic literature review (SLR) focused on the implementation of chatbots using Large Language Models (LLMs), aimed at providing insights into the architectures, frameworks, best practices, and evaluation metrics that are shaping the field. By analyzing 39 primary studies, the review addresses six key research questions, exploring common architectures such as client-server and Retrieval-Augmented Generation (RAG), and identifying frequently utilized models, including the GPT family, BERT, and open-source models like LLaMA. The paper evaluates the performance of these models across various domains, emphasizing the impact of fine-tuning, prompt engineering, and embedding techniques on accuracy and domain-specific relevance. Additionally, it highlights the critical evaluation metrics used in LLM-based chatbot systems, including accuracy, user satisfaction, content quality, safety, and efficiency. Ethical considerations, including data governance, bias mitigation, and fairness audits, are also discussed to ensure responsible deployment of LLM chatbots. The review concludes with an exploration of the trade-offs between performance, cost-efficiency, and scalability, providing a comprehensive framework for future research and development of LLM-based chatbot applications.