Pest control and disease management are significant challenges in agriculture, traditionally managed through labor-intensive methods that are often reactive rather than proactive. The integration of Artificial Intelligence (AI) in smart farming systems offers promising solutions to these challenges by enabling real-time monitoring, early detection, and automated responses. This paper explores the application of AI technologies, including machine learning (ML), computer vision, and Internet of Things (IoT) sensors, in detecting pests and diseases in crops. It proposes a comprehensive AI-driven framework for smart farming that combines real-time data collection, predictive analytics, and automated decision-making to enhance pest control and disease detection. The research demonstrates how AI enhances agricultural productivity, reduces chemical inputs, and promotes sustainability. Future directions and the ethical implications of AI adoption in farming are discussed, highlighting the need for ongoing research and responsible implementation.