Breast cancer poses a major health challenge worldwide, and identifying it early can improve treatment results. This study investigates the creation of a conversational chatbot that uses NLP techniques to aid in diagnosing breast cancer. By leveraging advanced NLP methods, the research aims to create an intelligent chatbot specifically for breast cancer diagnostics. The chatbot is designed to comprehend patient symptoms, gather important information, and offer initial diagnostic recommendations. Comparative analyses indicate that the GPT model emerged as the most effective for the chatbot’s development due to its advanced generative capabilities and fine-tuning for domain-specific tasks, achieving a remarkable 97.8% diagnostic accuracy. Experimental results showed that the GPT-based chatbot outperformed others in conversational coherence (BLEU score: 0.89), diagnostic accuracy (precision: 0.96, recall: 0.95), and natural language comprehension (semantic similarity score: 0.91). This research opens up opportunities for developing accessible and reliable tools for breast cancer detection by demonstrating how advanced NLP models can be used to create effective and user-friendly healthcare chatbots.