Considering the recent trend of population growth, the current worldwide crop productivity needs to be doubled by 2050. The prevalence of disease and insect infestations is one of the main obstacles in achieving this productivity goal. As a result, it is essential to develop effective techniques for the automatic detection, identification, and forecasting of pests and diseases in agricultural crops. In recent years, the agricultural sector has witnessed a transformative wave of technological advancements, particularly in the realms of artificial intelligence (AI) and machine learning (ML). Experimental results based on current data of pests and diseases have proved that the AI method is faster and more accurate than conventional and existing monitoring and forecasting procedures. By leveraging advanced algorithms and data-driven approaches, AI empowers farmers with early detection tools, enabling swift identification of pest infestations and diseases. Image recognition technologies, coupled with drones and smartphones, offer a proactive solution by capturing real-time data from fields, which AI algorithms analyze to pinpoint specific issues. Additionally, Internet of Things (IoT) devices equipped with sensors facilitate the collection of vital environmental data, paving the way for predictive modeling and precision agriculture practices. Moreover, AI-driven robotic systems equipped with precision spraying mechanisms minimize pesticide usage, reducing environmental impact and promoting sustainable farming practices. Collaborative efforts among researchers, technologists, and farmers have led to the creation of smart farming solutions that integrate AI and ML. This chapter highlights the transformative potential of AI and ML in reshaping pest and disease management paradigms, fostering a future where agriculture is not only productive and resilient but also ecofriendly and sustainable.