Energy　efficiency　and　security　are　critical　components　of　Quality　of　Service　(QoS)　and　remain　a　challenge　in　WSN-assisted　IoT　owing　to　its　open　and　resource-limited　nature.　Despite　intensive　research　on　WSN-IoT,　only　a　few　have　achieved　significant　levels　of　energy　efficiency　and　load　balancing　on　clustering　nodes.　This　study　proposes　a　novel　approach　for　dynamic　cluster-based　WSN-IoT　networks　to　enhance　the　network＇s　resilience　using　data　fusion　techniques　and　eliminate　illogical　clustering.　The　Mean　Value　and　Minimum　Distance　Method　identifies　the　optimal　cluster　heads　within　the　network　by　reducing　data　redundancy,　resulting　in　improved　quality　of　service,　energy　optimization,　and　enhanced　lifetime.　The　proposed　fused　deep　learning-based　data　mining　method　(RNN-LSTM)　mitigates　the　data　fitting　and　enhances　the　dynamic　routing　and　balancing　load　at　the　WSN　fusion　center.　The　novel　approach　splits　the　network　into　layers,　assigning　sensor　nodes　to　each　layer,　drastically　reducing　latency,　data　transfers,　and　the　fusion　center＇s　overhead.　Distinct　experiments　evaluated　the　suggested　approach＇s　efficacy　by　varying　the　hidden　layer　nodes　and　signaling　intervals.　The　empirical　verdicts　exhibit　that　the　presented　routing　algorithms　surpass　state-of-the-art　conventional　routing　systems　in　energy　depletion,　average　latency,　signaling　overhead,　cumulative　throughput,　and　route　heterogeneity.　©　2024　Elsevier　Ltd