The　Internet　of　Things　(IoT)　has　developed　a　well-defined　infrastructure　due　to　commercializing　novel　technologies.　IoT　networks　enable　smart　devices　to　compile　environmental　information　and　transmit　it　to　demanding　users　through　an　IoT　gateway.　The　explosive　increase　of　IoT　users　and　sensors　causes　network　bottlenecks,　leading　to　significant　energy　depletion　in　IoT　devices.　The　wireless　network　is　a　robust,　empirically　significant,　and　IoT　layer　based　on　progressive　characteristics.　The　development　of　energy-efficient　routing　protocols　for　learning　purposes　is　critical　due　to　environmental　volatility,　unpredictability,　and　randomness　in　the　wireless　network＇s　weight　distribution.　To　achieve　this　critical　need,　learning-based　routing　systems　are　emerging　as　potential　candidates　due　to　their　high　degree　of　flexibility　and　accuracy.　However,　routing　becomes　more　challenging　in　dynamic　IoT　networks　due　to　the　time-varying　characteristics　of　link　connections　and　access　status.　Hence,　modern　learning-based　routing　systems　must　be　capable　of　adapting　in　real-time　to　network　changes.　This　research　presents　an　intelligent　fault　detection,　energy-efficient,　quality-of-service　routing　technique　based　on　reinforcement　learning　to　find　the　optimum　route　with　the　least　amount　of　end-to-end　latency.　However,　the　cluster　head　selection　is　dependent　on　residual　energy　from　the　cluster　nodes　that　reduce　the　entire　network＇s　existence.　Consequently,　it　extends　the　network＇s　lifetime,　overcomes　the　data　transmission＇s　energy　usage,　and　improves　network　robustness.　The　experimental　results　indicate　that　network　efficiency　has　been　successfully　enhanced　by　fault-tolerance　strategies　that　include　highly　trusted　computing　capabilities,　thus　decreasing　the　risk　of　network　failure.