The　rapid　expansion　of　Internet　of　Things　(IoT)　devices　has　led　to　an　escalation　in　security　vulnerabilities,　particularly　concerning　botnet　attacks.　Securing　IoT　networks　against　botnet　threats　is　paramount　to　preserving　network　integrity　and　safeguarding　sensitive　data.　Despite　advancements　in　security　measures,　traditional　methods　often　fall　short　in　effectively　detecting　and　mitigating　botnet　activity　in　IoT　environments.　There　is　a　pressing　need　for　robust　and　adaptive　detection　mechanisms　capable　of　accurately　identifying　botnet　behavior　amidst　the　complexity　of　IoT　network　traffic.　This　research　addresses　the　challenge　of　botnet　detection　in　IoT　networks,　aiming　to　develop　an　effective　and　scalable　solution　that　can　accurately　discern　between　benign　and　IoT　botnets.　To　address　this　problem,　we　propose　the　use　of　ensemble　learning　techniques　for　the　IoT　botnet　detection.　Leveraging　the　N-BaIoT　dataset,　which　offers　real-world　IoT　traffic　data,　we　apply　the　Voting　Classifier　to　nine　distinct　IoT　devices　and　evaluate　its　performance　against　key　metrics　such　as　accuracy,　precision,　recall,　and　F1　score.　Our　experiments　demonstrate　the　effectiveness　of　the　proposed　ensemble　approach,　achieving　high　accuracy　with　an　average　accuracy　rate　of　99.3%.　Furthermore,　the　ensemble　method　exhibits　strong　precision,　recall,　and　F1　scores　across　various　IoT　device　types,　underscoring　its　efficacy　in　accurately　discerning　botnet　activity.　This　research　contributes　to　the　advancement　of　botnet　detection　in　IoT　networks　by　introducing　an　ensemble-based　approach　that　offers　robust　and　adaptive　detection　capabilities.　Our　findings　highlight　the　potential　of　ensemble　learning　techniques　in　enhancing　security　measures　in　IoT　environments.