The　Internet　of　Vehicles　(IoV)　transforms　the　automobile　industry　through　connected　vehicles　with　communication　infrastructure　that　improves　traffic　control,　safety　and　information,　and　entertainment　services.　However,　some　issues　remain,　like　data　protection,　privacy,　compatibility　with　other　protocols　and　systems,　and　the　availability　of　stable　and　continuous　connections.　Specific　problems　are　related　to　energy　consumption　for　transmitting　information,　distributing　energy　loads　across　the　vehicle＇s　sensors　and　communication　units,　and　designing　energy-efficient　approaches　to　processing　received　data　and　making　decisions　in　the　context　of　the　IoV　environment.　In　the　realm　of　IoV,　we　propose　OptiE2ERL,　an　advanced　Reinforcement　Learning　(RL)　based　model　designed　to　optimize　energy　efficiency　and　routing.　Our　model　leverages　a　reward　matrix　and　the　Bellman　equation　to　determine　the　optimal　path　from　source　to　destination,　effectively　managing　communication　overhead.　The　model　considers　critical　parameters　such　as　Remaining　Energy　Level　(REL),　Bandwidth　and　Interference　Level　(BIL),　Mobility　Pattern　(MP),　Traffic　Condition　(TC),　and　Network　Topological　Arrangement　(NTA),　ensuring　a　comprehensive　approach　to　route　optimization.　Extensive　simulations　were　conducted　using　NS2　and　Python,　demonstrating　that　OptiE2ERL　significantly　outperforms　existing　models　like　LEACH,　PEGASIS,　and　EER-RL　across　various　performance　metrics.　Specifically,　our　model　extends　the　network　lifetime,　delays　the　occurrence　of　the　first　dead　node,　and　maintains　a　higher　residual　energy　rate.　Furthermore,　OptiE2ERL　enhances　network　scalability　and　robustness,　making　it　a　superior　choice　for　IoV　applications.　The　simulation　results　highlight　the　effectiveness　of　our　model　in　achieving　energy-efficient　routing　while　maintaining　network　performance　under　different　scenarios.　By　incorporating　a　diverse　set　of　parameters　and　utilizing　RL　techniques,　OptiE2ERL　provides　a　robust　solution　for　the　challenges　faced　in　IoV　networks.　This　research　contributes　to　the　field　by　presenting　a　model　that　optimizes　energy　consumption　and　ensures　reliable　and　efficient　communication　in　dynamic　vehicular　environments.