The Food and Agriculture Organization reports that agricultural pests result in a 20–40% loss of worldwide crop output annually. Using insecticides excessively might cause serious consequences. Smart agriculture provides farmers with the best possible option for managing these harmful insect pests and disease spread by combining artificial intelligence (AI) and machine learning (ML) techniques with traditional approaches to agriculture. In this chapter, we will discuss briefly about the applications of AI and ML in pest and disease management. How they are beneficial for the improvement of crop yield and what are the challenges that are necessary to overcome? The use of AI and ML offers data-oriented resolutions to increase the yield of crops by investigating large datasets and providing accurate predictions of pest outbreaks and the spread of diseases. These technologies provide information related to climatic conditions, quality of soil, and crop health metrics and help precision agriculture focus on various specific points of pest infestations in the crop field to overcome the extensive usage of chemical pesticides and their harmful environmental impacts. However, there are certain challenges in implementing AI and ML strategies that need to be addressed in detail. The unavailability of large datasets, lack of skilled force, high cost, extreme climatic conditions, lack of digital resources, and government policies make these technologies difficult to implement for small-scale farmers. Besides these challenges, AI and ML models have the potential to control pest outbreaks and the spread of diseases. These technologies provide more sustainable and promising ways to minimize the use of chemicals and to meet the growing global demand for food.