Plant-parasitic nematodes inflict significant crop losses worldwide, estimated at over US $173 billion annually. Traditional diagnostic and management approaches are laborious and often lack precision. Artificial intelligence (AI) and machine learning (ML) can improve sustainable plant nematode diagnosis and management by automating identification and reducing chemical use. AI helps to predict outbreaks early, supporting targeted interventions and minimizing environmental impact. AI enhances precision agriculture, integration with emerging technologies, information technology, and smart farming, making farming practices more efficient and eco-friendly. There is ongoing research, and some methods are still being refined, so results may vary by context and crop type. This chapter synthesizes key developments in AI applications—ranging from deep learning-based image analysis to hyperspectral sensing and ML-driven decision support—highlighting achievements, remaining challenges, and future research directions.