Real-time disease and pest detection in agricultural crops has historically been a challenging task, relying on traditional methods such as visual inspection and trial and error. Similarly, in India in 2000, visual inspection was the primary method, with farmers reporting issues to the agricultural extension offices for confirmation and treatment recommendations, although pinpointing the specific cause was often difficult for effective and early detection. However, recent advancements in diagnostic tools like molecular tests and the integration of artificial intelligence (AI) have transformed the field, improving accuracy, timeliness, and control strategies. Molecular testing also aids in selecting the most suitable treatment by identifying the pathogen strains that are hard to spot. Real-time disease and pest detection is crucial for modern agriculture, thus preventing significant crop losses. The choice of technology depends on factors like farm size, crop type, and budget. AI-powered systems have the potential to revolutionize agriculture by using computer vision and machine learning to identify diseases and pests. Integration with Internet of Things (IoT) devices and collecting environmental data further enhances real-time detection and prompt preventive measures. By leveraging AI algorithms, such as convolutional neural networks (CNN) and Support Vector Machines, large volumes of data, including images and sensor readings, are analyzed to accurately identify crop issues. The other benefits include early detection, targeted interventions, reduced pesticide reliance, and valuable insights for proactive decision-making. By minimizing yield losses and optimizing crop management, AI contributes to more efficient and sustainable agriculture. Continued research and development in this field can further enhance efficient and sustainable crop cultivation practices.