In　the　dynamic　entertainment　industry,　predicting　a　movie＇s　opening　box　office　revenue　remains　critical　for　filmmakers　and　studios.　To　address　this　challenge,　we　present　a　novel　Cross-modal　transformer　and　a　Hierarchical　Fusion　Neural　Network　(CHFNN)　model　tailored　to　predict　movie　box　office　earnings　based　on　multimodal　features　extracted　from　movie　trailers,　posters,　and　reviews.　The　Cross-modal　Transformer　component　of　the　CHFNN　model　captures　intricate　inter-modal　relationships　by　performing　a　cross-modal　fusion　of　the　extracted　features.　It　employs　self-　attention　mechanisms　to　dynamically　weigh　the　importance　of　each　modality＇s　information.　This　allows　the　model　to　learn　to　focus　on　the　most　relevant　information　from　trailers,　posters,　and　reviews,　adapting　to　the　unique　characteristics　of　each　movie.　The　Hierarchical　Fusion　Neural　Network　within　CHFNN　further　refines　the　fused　features,　enabling　a　deeper　understanding　of　the　inherent　hierarchical　structure　of　multimodal　data.　By　hierarchically　combining　the　cross-　modal　features,　our　model　learns　to　capture　both　global　and　local　interactions,　enhancing　its　predictive　capacity.　We　evaluate　the　performance　of　the　CHFNN　model　on　a　comprehensive　Internet　Movie　Dataset　by　obtaining　metadata　for　50,186　movies　from　the　1990s　to　2022,　which　includes　movie　trailers,　posters,　and　review　data.　Our　results　demonstrate　that　the　CHFNN　model　outperforms　existing　models　in　prediction　accuracy,　achieving　95.80%　prediction　accuracy.　The　CHFNN　model　provides　state-of-the-art　predictive　power　and　offers　interpretability　through　attention　mechanisms,　allowing　insights　into　the　factors　contributing　to　a　movie＇s　box　office　success.