The early detection of anomalies associated with crop diseases can be achieved through an advanced method that employs artificial intelligence (AI). AI-enabled systems have the capability to identify subtle variations from typical patterns in crop canopies or leaves following infestations or injuries, thereby preventing more problems. This chapter explores the use of AI techniques, including machine learning and deep learning, to enhance the early detection of anomalies in crop diseases and its symptoms. Deep learning models have the capability to integrate information from a diverse array of sources, such as spectral data, sensor readings, satellite imagery, and close-range photographs, to assess crop health, evaluate soilSoil quality, and analyze surrounding environmental conditions and associated parameters. These models are capable of identifying unusual patterns in images or spectral data, which allows for the detection and classification of disease spread ranging from initial to severe levels, as well as pest invasions, nematode attacks, and nutrient deficiencies. Early detection of crop stresses allows for targeted precise treatments, reducing farm expense, resource waste and minimizing crop loss, meanwhile increasing sustainable productivity. Moreover, AI systems can sense environmental influences involving in crop production such as soil temperature, soil moisture, soil chemicals, irrigation demands, and weather patterns, providing insights for better crop management practices, such as optimal irrigation and nutrient application. The key advantage of AI-enabled anomaly detection in crop production is its capacity to rapidly deal vast data and deliver real-time actionable prescriptions, with no human intervention. However, constrains such as quality of data/ datasets, DL model accuracy, and deployable system integration need attention for better results/outcomes. As the farming sector becomes increasingly data-driven, AI plays a role in improving decision-making and operational efficiency to expand or altering suitable farming practices based on needs and geographical resources. This chapter also signifies transformative possibilities of AI in sustainable agriculture based on UN SDGs, including increased crop productivity, eco-sustainability, and food security.