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Multilingual chatbot development has gained significant traction with the advent of pre-trained language models (PLMs), which facilitate seamless cross-lingual communication across diverse sectors such as healthcare, education, finance, and customer service. Traditional chatbot frameworks, often based on rule- based or statistical approaches, tend to struggle with linguistic diversity, contextual understanding, and low- resource language support. The integration of PLMs has transformed this landscape by offering scalable, data- driven solutions capable of comprehending and generating meaningful responses in multiple languages with high accuracy. This survey explores the critical role of PLMs in enabling effective multilingual chatbot development and deployment. We examine advanced methodologies, including domain-specific fine-tuning strategies, knowledge graph integration, transfer learning, and optimization techniques such as adapter-based fine-tuning, sparse tuning, and knowledge distillation. Figures included in the paper illustrate various architectural, technical, and methodological frameworks implemented in recent studies. Furthermore, this paper investigates the role of interactive machine translation, user behavior analysis, and ethical considerations such as transparency, bias mitigation, and cultural sensitivity in chatbot design. Challenges such as computational inefficiency, cultural and linguistic biases, domain adaptability, and limited resources for underrepresented languages are also highlighted