Large Language Models (LLMs) have emerged as transformative tools in healthcare, offering unprecedented capabilities in data analysis and predictive modeling. This paper investigates the application of LLMs in predicting future illnesses through a detailed case study. Leveraging electronic health records (EHRs) and other patient data, an advanced LLM was employed to forecast the likelihood of developing conditions such as diabetes and cardiovascular diseases. The study demonstrated high predictive accuracy, with the LLM correctly identifying at-risk patients based on their medical histories and lifestyle factors. The model predicted the onset of diabetes with a precision of 92% and cardiovascular diseases with an accuracy of 89%. These findings underscore the potential of LLMs to significantly enhance early diagnosis and preventive care. However, the integration of LLMs into clinical practice requires addressing several challenges. High-quality, comprehensive datasets are essential for accurate predictions, and ethical concerns related to patient privacy and data security must be carefully managed. Additionally, the interpretability of LLM predictions remains a critical issue, necessitating further research into developing transparent and explainable AI models. This study highlights the transformative potential of LLMs in predictive healthcare, suggesting pathways for their integration into clinical practice to improve patient outcomes and healthcare efficiency.