As　influenza　viruses　mutate　rapidly,　a　prediction　model　for　potential　outbreaks　of　influenza-like　illnesses　helps　detect　the　spread　of　the　illnesses　in　real　time.　In　order　to　create　a　better　prediction　model,　in　this　study,　in　addition　to　using　the　traditional　hydrological　and　atmospheric　data,　features,　such　as　popular　search　keywords　on　Google　Trends,　public　holiday　information,　population　density,　air　quality　indices,　and　the　numbers　of　COVID-19　confirmed　cases,　were　also　used　to　train　the　model　in　this　research.　Furthermore,　Random　Forest　and　XGBoost　were　combined　and　used　in　the　proposed　prediction　model　to　increase　the　prediction　accuracy.　The　training　data　used　in　this　research　were　the　historical　data　taken　from　2016　to　2021.　In　our　experiments,　different　combinations　of　features　were　tested.　The　results　show　that　features,　such　as　popular　search　keywords　on　Google　Trends,　the　numbers　of　COVID-19　confirmed　cases,　and　air　quality　indices　can　improve　the　outcome　of　the　prediction　model.　The　evaluation　results　showed　that　the　error　rate　between　the　predicted　results　and　the　actual　number　of　influenza-like　cases　form　Week　15　to　Week　18　fell　to　less　than　5%.　The　outbreak　of　COVID-19　in　Taiwan　began　in　Week　19　and　resulted　in　a　sharp　rise　in　the　number　of　clinic　or　hospital　visits　by　patients　of　influenza-like　illnesses.　After　that,　from　Week　21　to　Week　26,　the　error　rate　between　the　predicted　and　actual　numbers　of　influenza-like　cases　in　the　later　period　dropped　down　to　13%.　It　can　be　confirmed　from　the　actual　experimental　results　in　this　research　that　the　use　of　the　ensemble　learning　prediction　model　proposed　in　this　research　can　accurately　predict　the　trend　of　influenza-like　cases.