In　intelligent　transportation,　road　maintenance　is　a　critical　component　that　holds　significant　importance　for　both　road　longevity　and　driving　safety.　The　longevity　of　road　surfaces　is　influenced　by　various　factors,　including　temperature,　humidity,　sunlight,　axle　loads,　and　more.　By　monitoring　and　adjusting　environmental　factors,　it　is　possible　to　maintain　road　surfaces　in　suitable　working　conditions,　thereby　extending　their　lifespan.　This　study　employs　an　LSTM　model　to　model　the　road　aging　problem　under　the　influence　of　multiple　factors.　Additionally,　a　multiple　linear　regression　model　is　utilized　to　model　the　impact　of　maintenance　actions　on　environmental　factors.　Additionally,　an　AIoT-based　intelligent　road　maintenance　solution　is　proposed.　Furthermore,　this　research　incorporates　data　augmentation　and　integration　techniques　to　enhance　and　consolidate　previous　road　aging　data　under　the　influence　of　individual　factors.　Subsequently,　a　comparative　experiment　on　road　aging　is　conducted.　The　experimental　results　demonstrate　that　the　intelligent　road　maintenance　system　designed　in　this　study　can　effectively　extend　the　lifespan　of　roads　without　increasing　maintenance　costs.　This　outcome　provides　new　insights　and　possibilities　for　building　smart　cities.　©　2023　IEEE.