Abstract Disruptive technologies can reconfigure innovation trajectories and create new market opportunities, yet their early detection remains difficult because disruptive impact is uncertain and often becomes visible only years after invention. Building on the CD disruptiveness measure derived from citation-network dynamics, this study develops an interpretable machine-learning framework to screen and prioritize potentially disruptive patent candidates ex ante.