Increases　in　population　and　prosperity　are　linked　to　a　worldwide　rise　in　garbage.　The　＂classification＂　and　＂recycling＂　of　solid　waste　is　a　crucial　tactic　for　dealing　with　the　waste　problem.　This　paper　presents　a　new　twolayer　intelligent　decision　system　for　waste　sorting　based　on　fused　features　of　Deep　Learning　(DL)　models　as　well　as　a　selection　of　an　optimal　deep　Waste-Sorting　Model　(WSM)　based　on　Multi-Criteria　Decision　Making　(MCDM).　A　dataset　comprising　1451　samples　of　images　of　waste,　distributed　across　four　classes　-　cardboard　(403),　glass　(501),　metal　(410),　and　general　trash　(137),　was　used　for　sorting.　This　study　proposes　a　Multi-Fused　Decision　Matrix　(MFDM)　based　on　identified　fusion　score　level　rules,　evaluation　criteria,　and　deep　fused　waste-sorting　models.　Five　fusion　rules　used　in　the　sorting　process　and　the　evaluation　perspectives　into　the　MFDM　are　sum,　weighted　sum,　product,　maximum,　and　minimum　rules.　Additionally,　each　of　entropy　and　Visekriterijumska　Optimizacija　i　Kompromisno　Resenje　in　Serbian　(VIKOR)　methods　was　used　for　weighting　selected　criteria　as　well　as　ranking　deep　WSMs.　The　highest　accuracy　rate　of　98%　was　scored　by　ResNet50-GoogleNet-　Inception　based　on　the　minimum　rule.　However,　under　the　same　rule,　an　insufficient　accuracy　rate　of　sorting　was　presented　by　ResNet50-GoogleNet-Xception.　Since　Qi　=　0　for　Inception-Xception,　the　final　output　based　on　MCDM　methods　indicates　that　the　fused　Inception-Xception　model　outperforms　the　other　fused　deep　WSMs,　which　achieved　the　lowest　values　of　Qi.　Thus,　Inception-Xception　was　chosen　as　the　best　deep　waste-sorting　model　based　on　images　of　waste,　multiple　evaluation　criteria,　and　different　fusion　perspectives.　The　mean　and　standard　deviation　metrics　were　both　used　to　validate　the　selection　findings　objectively.　The　suggested　approach　can　aid　urban　decisionmakers　in　prioritizing　and　choosing　an　Artificial　Intelligence　(AI)-optimized　optimal　sorting　model.