Due　to　rapid　change　in　influenza　viruses,　a　prediction　model　for　outbreaks　of　influenza-like　illnesses　helps　to　find　out　the　spread　of　the　illnesses　in　real　time.　In　addition　to　using　traditional　hydrological　and　atmospheric　data,　popular　search　keywords　on　Google　Trends　are　used　as　features　in　this　research.　Google　Trends　are　popular　keyword　searches　on　the　Google　search　engine.　Popular　keywords　used　in　discussions　of　influenza-like　symptoms　at　specific　regions　within　specific　periods　are　used　in　this　research.　Public　holiday　information　in　Taiwan,　the　population　density,　air　quality　indices,　and　the　numbers　of　COVID-19　confirmed　cases　are　also　used　as　features　in　this　research.　An　Ensemble　Learning　model,　combining　Random　Forest　and　XGBoost,　is　used　in　this　research.　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.　The　evaluation　results　show　that　the　mean　RMSLE　of　our　proposed　model　is　0.2　in　comparison　with　the　actual　number　of　influenza-like　cases.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.