The　Internet　of　Things　(IoT)　landscape　is　witnessing　rapid　growth,　driven　by　continuous　innovation　and　a　simultaneous　increase　in　cybersecurity　threats.　As　these　threats　become　more　sophisticated,　the　imperative　to　fortify　IoT　devices　against　emerging　vulnerabilities　becomes　increasingly　pronounced.　This　research　is　motivated　by　the　need　for　comprehensive　IoT　threat　detection　solutions　that　can　effectively　address　the　evolving　threat　landscape.　Existing　approaches　often　fall　short　in　adapting　to　the　dynamic　nature　of　IoT　environments　and　the　increasing　complexity　of　attacks.　The　core　problem　addressed　in　this　research　is　the　development　of　a　novel　Hybrid　Convolutional　Neural　Network　and　Long　Short-Term　Memory　(CNN-LSTM)　architecture　tailored　for　precise　and　efficient　IoT　threat　detection.　This　architecture　aims　to　overcome　the　limitations　of　existing　methods　and　enhance　the　security　of　IoT　ecosystems.　Our　study　encompasses　a　detailed　analysis　of　the　proposed　Hybrid　CNN-LSTM　model,　leveraging　data　from　diverse　datasets,　including　IoT-23,　N-BaIoT,　and　CICIDS2017.　The　proposed　model　is　tested　and　validated　on　more　than　14　attack　types.　We　have　designed　this　model　to　exhibit　robust　threat　detection　capabilities　by　effectively　capturing　and　analyzing　IoT　security　data.　The　outcomes　of　our　research　showcase　remarkable　accuracy,　with　the　models　achieving　95%　accuracy　on　the　IoT-23　dataset　and　an　outstanding　99%　accuracy　on　both　the　N-BaIoT　and　CICIDS2017　datasets.　These　findings　underscore　the　model＇s　adaptability　to　various　IoT　environments.　Our　research　contributes　a　comprehensive　Hybrid　CNN-LSTM　architecture　that　significantly　enhances　IoT　threat　detection.　We　introduce　Principal　Component　Analysis　(PCA)　to　optimize　data　processing　and　incorporate　advanced　optimization　techniques　like　model　quantization　and　pruning　to　improve　deployment　efficiency　in　resource-constrained　IoT　environments.　This　study　lays　the　foundation　for　future　advancements　in　bolstering　IoT　security.　©　2024　The　Author(s)