The current context of health systems is characterized by a rapid evolution of technology and an increasing demand for health services. There is an increasing emphasis on preventive care and the use of data and technology to improve the efficiency and effectiveness of healthcare delivery. Currently, there is an evolution of digital transformation and the integration of natural language processing and big data analysis has become very important to revolutionize various fields, including the field of health. This article explores the synergies between NLP and big data in the context of predictive disease diagnosis, aiming to elucidate how these technologies can improve early disease detection, treatment, and management. By exploring different NLP techniques, such as sentiment analysis and text classification, combined with big data analysis methodologies, the article presents how predictive models can be developed to anticipate the onset and progression of diseases. Through this paper, you will find the potential of combining natural language processing with big data to diagnose patients' diseases more quickly, which offers a path to personalized medicine and ultimately to a healthier society. Through this paper, various NLP and big data techniques are presented with brief descriptions. The dataset is described and preprocessed for analysis. Word cloud visualization highlights prevalent disease symptoms, while latent Dirichlet allocation categorizes symptoms by disease. Additionally, TF-IDF vectorization and K-means clustering are employed for symptom clustering. The silhouette score, calculated at 0.7567, underscores the quality of the analysis.