In an age where conversations with machines are becoming as common as chats with friends, mental health chatbots stand at the forefront of today’s technology-driven world for improving patient care and optimizing medical procedures. The deployment of mental health chatbots presents significant challenges and a critical responsibility to uphold strict data privacy and security standards. A breach of data privacy can trigger severe implications such as monetary loss, legal repercussions, and even the closure of a business. On the other hand, security breaches often extend beyond immediate monetary damage, potentially leading to identity theft, operational disruptions, and lasting economic setbacks. To overcome these challenges and enhance data privacy and security, we introduce an innovative approach that integrates: 1) Parameter-efficient fine-tuning of a pre-trained Large Language Model (LLM) on a curated mental health dataset, 2) End-to-End Privacy mechanisms to protect sensitive patient data, and 3) Retrieval Augmented Generation (RAG) to enhance contextual awareness and improve the quality of LLM-generated responses. We conduct a thorough evaluation of our developed chatbot’s performance using a comprehensive set of metrics. Through the integration of these advancements, our goal is to build a secure and efficient LLM-based chatbot that enhances the accessibility and quality of mental healthcare while proactively addressing key privacy and security challenges.