This　paper　presents　a　vehicle　route　planning　method　based　on　game　theory　principles　and　innovative　utility　functions.　By　addressing　the　complexities　of　real-Time　traffic　congestion,　the　proposed　framework　offers　a　dynamic　allocation　strategy　for　rational　decision-making.　The　utility　function,　which　inte-grates　traffic　flow　volume,　road　capacity,　and　congestion　effects,　provides　accurate　travel　time　estimations.　Mathematical　analysis　and　validation,　including　genetic　algorithms,　underscore　the　framework＇s　robustness.　Equilibrium　solutions　reveal　allocation　strategies　responsive　to　varying　road　conditions.　Comparative　scenarios　demonstrate　the　utility　function＇s　effectiveness　in　guiding　enterprises＇　decisions.　This　research　extends　beyond　static　models,　envisioning　a　future　of　data　integration,　multi-objective　optimization,　adaptive　learning,　and　eco-friendly　navigation.　By　converging　these　routes,　the　paper　sets　the　stage　for　a　smarter,　more　sustainable　transportation　landscape.　©　2024　IEEE.