Due　to　climate　changes,　global　warming,　and　the　recent　drought,　forecasting,　checking,　and　analyzing　maximum　temperatures　as　one　of　the　foremost　imperative　climatic　parameters　allows　planners　to　plan　and　provide　the　necessary　arrangements.　The　main　reasons　for　checking　the　temperature　as　a　parameter　influencing　nature　are　agriculture,　pests,　diseases,　melting　ice　and　flooding,　evaporation　and　transpiration,　and　drought.　Today,　artificial　neural　networks　are　used　to　predict　time　series　like　temperature　because　of　their　feature　for　understanding　the　random　mechanism　of　fully　nonlinear　and　complex　series.　This　study　used　data　from　1953　to　2005,　two　methods,　and　multi-layer　perceptron　artificial　neural　networks　with　the　learning　algorithm　after　the　error　propagation　to　analyze　and　check　the　monthly　maximum　temperature.　This　issue　used　an　input　layer,　five　hidden　layers　of　TANSIG,　and　an　output　layer　of　the　pure　line　for　artificial　neural　networks.　The　mean　squared　error　criterion　was　also　used　to　assess　the　results.　In　the　following　study,　70%　of　the　total　data　were　used　as　training　data　(RMSE　=　1.8622　and　MSE　=　3.4677);　in　order　to　avoid　the　phenomenon　of　the　over-load　network,　15%　of　the　data　were　used　for　validation　data　(RMSE　=　1.7667　and　MSE　=　3.1213).　The　remaining　15　percent　has　also　been　used　to　check　and　test　data.　(RMSE　=　2.134　and　MSE　=　4.5538).　A　comparison　of　monthly　maximum　temperature　forecast　results　for　1953　and　2005　with　observed　data　shows　good　agreement　of　the　model.　The　overall　results　indicate　that　approximately　every　64　years　will　add　a　degree　to　the　temperature.　©　2022　IEEE.