As　the　Internet　of　Things　(IoT)　becomes　more　and　more　intelligent,　a　new　computing　paradigm,　predictive　intelligence　is　incorporated　into　many　IoT　applications.　The　devices　of　predictive　intelligence　in　IoT　applications　must　consider　the　power　and　delay　consumption.　As　the　Power-Driven　X-Routing　(PDXR)　problem　model　under　the　advanced　semiconductor　design,　the　length-restricted　condition　in　the　multi-dynamic　voltage　model　is　introduced　to　save　power　consumption　and　the　X-architecture　is　introduced　to　better　reduce　the　wirelength　to　optimize　the　chip　delay.　To　this　end,　an　effective　particle　swarm　optimization-based　power-driven　length-restricted　X-routing　algorithm　is　proposed　for　predictive　intelligence　in　IoT　applications.　Firstly,　a　pre-calculated　lookup　table　is　designed　to　provide　fast　information　query　for　the　subsequent　algorithm　flow.　Secondly,　an　improved　particle　swarm　optimization　algorithm　is　presented　for　the　discrete　PDXR　problem.　Thirdly,　in　the　adjustment　phase,　the　choice　of　intermediate　nodes　is　expanded,　and　is　no　longer　limited　to　the　corner　points　of　obstacles.　Fourthly,　a　removal　strategy　of　redundant　points　is　proposed　to　optimize　the　routing　path.　Finally,　the　wirelength　is　further　reduced　by　a　local　topology　optimization　strategy.　The　experimental　results　show　that　the　proposed　algorithm　can　achieve　the　best　wirelength　cost　at　a　very　fast　speed　under　the　constraint　of　restricted　wirelength,　so　as　to　better　satisfy　the　demand　of　the　power　and　delay　performance　of　semiconductor　design　for　predictive　intelligence　in　IoT　applications.　©　2021　Elsevier　B.V.