Demand　forecasting　a　film＇s　opening　weekend　box　office　revenue　is　a　difficult　and　complex　task　that　decision-makers　face　due　to　a　lack　of　historical　data　and　various　complex　factors.　We　proposed　a　novel　Deep　Multimodal　Feature　Classifier　Neural　Network　model　(DMFCNN)　for　predicting　a　film＇s　opening　weekend　box　office　revenue　using　deep　multimodal　visual　features　extracted　from　movie　posters　and　movie　metadata.　DMFCNN　is　an　end-to-end　predictive　model　that　fuses　two　different　feature　classifiers＇　predictive　power　in　estimating　the　movie　box　office　revenue.　Initially,　a　pre-trained　residual　convolutional　neural　network　(ResNet50)　architecture　using　transfer　learning　techniques　extracts　visual,　and　object　representations　learned　from　movie　posters.　The　movie　posters＇　discriminative　and　financial　success-related　features　are　combined　with　other　movie　metadata　to　classify　the　movie　box　office　revenue.　The　proposed　DMFCNN　aided　in　developing　a　robust　predictive　model　that　jointly　learns　and　defines　useful　revenue-related　poster　features　and　objects　semantics,　which　strongly　correlates　with　movie　box　office　revenue　and　aesthetic　appearance.　Although　our　main　task　was　classification,　we　also　analyzed　regressions　between　our　exogenous　variables　as　a　regularizer　to　avoid　the　risk　of　overfitting.　We　evaluated　DMFCNN＇s　performance　and　compared　it　to　various　state-of-the-art　models　on　the　Internet　Movie　Database　by　collecting　49,857　movies　metadata　and　posters　from　2006　to　2019.　The　learned　information　on　movie　posters　and　predicted　outcomes　outperformed　existing　models,　achieving　59.30%　prediction　accuracy.　The　proposed　fusion　strategy　outperformed　the　existing　fusion　schemes　in　precision,　Area　Under　Cover,　sensitivity,　and　specificity　by　achieving　80%,　81%,　79%,　and　78%,　respectively.