Decision support systems (DSS) for plant-parasitic nematodes are an active area of development. DSS integrate data acquisition, modeling, and recommendation modules to aid farmers in timely and precise interventions. Current tools combine agronomic models (rotations, damage thresholds) with growing computational support (machine learning, simulation). Real-world deployments are most advanced in high-value crops like potato and sugar beet in Europe, but new sensing and AI technologies promise broader applicability. Continued integration of diverse data (soil, remote sensing, genomics, and climate) and user-friendly platforms (web/mobile apps) are driving DSS toward making nematode management both more precise and more practical for growers. This chapter surveys the development of DSS for PPN management, categorizes major system architectures, highlights recent AI- and simulation-based tools, and discusses case studies such as NemDST, NemaDecide, and SBN-Watch. Key challenges and future research directions are identified to guide the deployment of efficient and scalable DSS solutions.