Machine learning (ML) models, popularly known as artificial intelligence (AI), can complete extraordinary tasks at a superlative pace compared to a human being. Applications of AI in various aspects of human life have seen a sudden surge over the last decade. Like in many other areas, the application of AI in healthcare, such as in disease diagnosis, drug discovery, and patient risk identification, has proven tremendous benefits, hence gradually becoming inevitable. That warrants that healthcare professionals in the near future will need a certain level of understanding of how AI, more specifically ML models, are built and how a model performs the given task in a clinical setting. This chapter aims to introduce AI, the fundamental concept behind machine learning models, and how to develop such a model for healthcare applications, particularly for the diagnosis of periodontitis. While showing the steps in building an ML model, the Gradient Boosting Classifier (GBC) model was found to be more accurate (93.5%) compared to the K-Nearest Neighbor (KNN) model (61.3%) in diagnosing periodontitis using a clinically comparable dataset comprising average periodontal pocket depth, bleeding score, clinical attachment loss, plaque score, and tooth loss of individuals, of whom 9 are periodontally healthy, 20 have gingivitis, and 232 have different stages of periodontitis. The chapter will work as a guide for both dentists and AI scientists who are interested in developing AI for use in patient care, diagnosis, and assistive tools for researchers and practitioners seeking to revolutionize digital dentistry through the power of machine learning.