The　Internet　of　Things　(IoT)　has　transformed　many　aspects　of　modern　life,　from　healthcare　and　transportation　to　home　automation　and　industrial　control　systems.　However,　the　increasing　number　of　connected　devices　has　also　led　to　an　increase　in　security　threats,　particularly　from　botnets.　To　mitigate　these　threats,　various　machine　learning　(ML)　and　deep　learning　(DL)　techniques　have　been　proposed　for　IoT　botnet　attack　detection.　This　systematic　review　aims　to　identify　the　most　effective　ML　and　DL　techniques　for　detecting　IoT　botnets　by　delving　into　benchmark　datasets,　evaluation　metrics,　and　data　pre-processing　techniques　in　detail.　A　comprehensive　search　was　conducted　in　multiple　databases　for　primary　studies　published　between　2018　and　2023.　Studies　were　included　if　they　reported　the　use　of　ML　or　DL　techniques　for　IoT　botnet　detection.　After　screening　1,567　records,　25　studies　were　included　in　the　final　review.　The　findings　suggest　that　ML　and　DL　techniques　show　promising　results　in　detecting　IoT　botnet　attacks,　outperforming　traditional　signature-based　methods.　However,　the　effectiveness　of　the　techniques　varied　depending　on　the　dataset,　features,　and　evaluation　metrics　used.　Based　on　the　synthesis　of　the　findings,　this　review　proposes　a　taxonomy　for　ML　and　DL　techniques　in　IoT　botnet　attack　detection　and　provides　recommendations　for　future　research　in　this　area.　This　review　illuminates　the　considerable　potential　of　ML　and　DL　approaches　in　bolstering　the　detection　of　IoT　botnet　attacks,　thereby　offering　valuable　insights　to　researchers　involved　in　the　domain　of　IoT　security.