Accurate counting, diagnosis, and identification of plant-parasitic nematodes (PPNs) are critical for effective management of these cryptic pests. Traditional counting methods and diagnosis based on morphological and molecular assays, while reliable, are labor-intensive and time-consuming and require specialized expertise. Recent advances in artificial intelligence (AI)—particularly machine learning and deep learning (DL)—offer automated, high-throughput alternatives for nematode counting, detection, classification, and quantification. This chapter synthesizes current AI-driven approaches in PPN counting, diagnosis, and identification, covering convolutional neural networks, object detection architectures (You Only Look Once variants), image-based methods, hyperspectral and remote sensing integration, and decision-support tools. Key algorithms, performance metrics, implementation challenges, and future directions for AI in nematology have also been discussed.