The healthcare sector is rapidly developing, and data mining has been widely used in disease prediction and prevention. The chapter presents insights into the techniques in the domain of data mining that are transforming disease prediction. The complex and voluminous healthcare data facilitate disease prediction before clinical manifestation. After providing an overview of the chapter, which is a brief description of data mining followed by stating the objectives of the chapter, at the beginning of the chapter, the author offers an account of data mining principles that are pertinent in the realm of healthcare. It gives a straightforward idea concerning the approaches involved in the accurate diagnosis of disease models by explaining the data preparation, feature extraction out of the supplied data, and data reduction. The subsequent analysis of disease prediction describes machine learning approaches’ strengths, weaknesses, and uses in detail. The narrative is enriched with the inclusion of illustrative case studies. In addition, this chapter gives an account of a prevalent field of deep learning for disease prediction that has incredible potential for complex medical data and image interpretation. The current advancement in the ML and DL techniques for developing disease prediction models has improved the early diagnosis of diseases. Furthermore, the chapter focuses on explaining ethical concerns and issues arising if data mining is used in disease prediction. Combining theoretical approaches, methods, and examples, the chapter is useful for academicians, practitioners, and individuals skilled in using data mining science for the prediction of early diseases and the prevention of concerning phenomena in the field of healthcare.