The　burden　distribution　process　is　an　important　and　efficient　measure　to　maintain　the　stable　operation　of　the　blast　furnace.　An　accurate　burden　distribution　model　will　reveal　the　impact　on　the　internal　furnace　state　and　help　to　optimize　the　blast　furnace　production　index.　This　article　reviews　the　recent　development　of　the　modeling　and　control　techniques　in　the　burden　distribution　process.　The　current　modeling　methods　of　the　blast　furnace　burden　distribution　can　mainly　be　divided　into　the　following　types:　the　mechanism　based　method,　the　physical　scale　model-based　experiments　and　the　data-driven　method.　However,　most　of　the　existing　modeling　methods　are　not　applicable　to　general　blast　furnaces　because　it　depends　on　the　specific　furnace　structure　and　parameters.　Furthermore,　with　the　advancement　in　measurement　technology,　sensors　now　provide　rich　amount　of　online　measurement　of　the　blast　furnace　iron-making　process.　This　makes　the　data　analysis　more　challenging.　It　is　imperative　to　establish　new　modeling　methods　for　the　burden　distribution　process.　Therefore,　this　paper　points　out　the　new　trends　in　modeling　and　control　of　the　blast　furnace　burden　distribution　process.　First,　a　dynamic　clustering　method　based　on　dynamic　time　warping　and　adaptive　resonance　theory　is　introduced.　Second,　the　inverse　dynamic　model-based　burden　distribution　control　is　developed.　Furthermore,　a　multi-model-based　switch　for　modeling　the　fluctuating　blast　furnace　process　is　formulated.　Finally,　the　reinforcement　learning　method　for　the　dynamic　optimization　of　the　production　index　is　recommended.