Anxiety is a widespread mental health concern, yet barriers such as stigma, financial constraints, and limited access to professional support prevent timely intervention. Advances in natural language processing (NLP) and artificial intelligence (AI) present a scalable and accessible solution for delivering personalized mental health support. This paper introduces an AI-powered chatbot system that integrates Dialogflow, OpenAI’s generative models, and Firebase to provide real-time, adaptive responses tailored to users’ emotional states. The system employs a hybrid chatbot model, combining rule-based intent recognition with generative AI to ensure conversational accuracy and contextual adaptability. PythonAnywhere serves as the central hosting platform, managing webhook-based communication between Dialogflow, OpenAI, and Firebase, enabling seamless data flow, real-time processing, and scalability. By leveraging PythonAnywhere’s cloud infrastructure, the system maintains low-latency interactions, ensuring efficient execution of NLP-driven interventions. Usability testing and participant feedback validate the chatbot’s effectiveness in delivering empathetic, AI-driven support, including calming exercises, affirmations, and anxiety management techniques. By demonstrating the potential of AI-enhanced conversational agents in mental health applications, this paper highlights the role of NLP, cloud-based architectures, and hybrid AI models in expanding digital mental health accessibility and improving engagement through intelligent, real-time interactions.