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mycotoxin contamination agricultural commodities global public health problem remained elusive various mitigation approaches particularly developing countries climate change impact exacerbates south africas vulnerability mycotoxin contamination significantly threatens itss food systems public health agroeconomic development herein analyse sixteen years 2005200620202021 annual national meteorological data south africa reveals systematic erratic variability critical climatic factors known influence mycotoxin contamination crops within study period data fumonisin fb monitoring show clear climatedependent trends strongest positive warming trend observed 20182019 20192020 051 cyear strong positive correlation likewise established fb contamination temperature r ranging 06 09 four machine learning models viz support vector machines extreme gradient boosting random forest orthogonal partial least squares generalized historical data suitable performance rmse low 000 adopted models able predict future fb contamination patterns reasonable precision r2 ranging 034 100 important model feature predicting average fb contamination ya historical pattern average fb contamination maize within region fbsavg two significant features modelling maximum fb contamination ym minimum temperature cmip6 data protempmin observed precipitation cru data oprep study provides strong evidence impact climate change fb south africa reiterates significance machine learning modelling predicting mycotoxin contamination light changing climatic conditions could facilitate early warnings adoption relevant mitigation measures could help mycotoxin risk management control