Purpose:　The　transportation　of　fresh　food　requires　cold　chain　logistics　to　maintain　a　low-temperature　environment,　which　can　reduce　food　waste　and　ensure　product　safety.　Therefore,　temperature　control　is　a　major　challenge　that　cold　chain　logistics　face.　Design/methodology/approach:　This　research　proposes　a　prediction　model　of　refrigerated　truck　temperature　and　air　conditioner　status　(air　speed　and　air　temperature)　based　on　hybrid　mayfly　algorithm　(MA)　and　extreme　learning　machine　(ELM).　To　prove　the　effectiveness　of　the　proposed　method,　the　mayfly　algorithm–extreme　learning　machine　(MA-ELM)　is　compared　with　the　traditional　ELM　and　the　ELM　optimized　by　classical　biological-inspired　algorithms,　including　the　genetic　algorithm　(GA)　and　particle　swarm　optimization　(PSO).　The　assessment　is　conducted　through　two　experiments,　including　temperature　prediction　and　air　conditioner　status　prediction,　based　on　a　case　study.　Findings:　The　prediction　method　is　evaluated　by　five　evaluation　indicators,　including　the　mean　relative　error　(MRE),　mean　absolute　error　(MAE),　mean　squared　error　(MSE),　root　mean　square　error　(RMSE)　and　coefficient　of　determination　(R2).　It　can　be　concluded　that　the　biological　algorithm,　especially　the　MA,　can　improve　the　prediction　accuracy.　This　result　clearly　proves　the　effectiveness　of　the　proposed　hybrid　prediction　model　in　revealing　the　nonlinear　patterns　of　the　cold　chain　logistics　temperature.　Research　limitations/implications:　The　case　study　illustrates　the　effectiveness　of　the　proposed　temperature　prediction　method,　which　helps　to　keep　the　product　fresh.　Even　though　the　performance　of　MA　is　better　than　GA　and　PSO,　the　MA　has　the　disadvantage　of　premature　convergence.　In　the　future,　the　modified　MA　can　be　designed　to　improve　the　performance　of　MA-ELM.　Originality/value:　In　prior　studies,　many　scholars　have　conducted　related　research　on　the　subject　of　temperature　monitoring.　However,　this　monitoring　method　can　only　identify　temperature　deviations　that　have　occurred　that　harmed　fresh　food.　As　a　countermeasure,　research　on　the　temperature　prediction　of　cold　chain　logistics　that　can　actively　identify　temperature　changes　has　become　the　focus.　Once　a　temperature　deviation　is　predicted,　temperature　control　measures　can　be　taken　in　time　to　resolve　the　risk.　©　2022,　Emerald　Publishing　Limited.