The　suddenness　and　obscurity　of　arc　faults　significantly　complicate　the　detection　and　prediction　of　resultant　fires,　posing　a　serious　threat　to　power　systems　and　public　safety.　Accurate　prediction　and　timely　identification　of　fires　induced　by　arc　faults　are　essential.　This　article　proposes　a　novel　approach　for　arc　fault　fire　prediction　by　fusing　electrical　signals　with　fire　images,　instead　of　relying　solely　on　electrical　signal　detection.　A　fire　data　collection　system　is　developed　based　on　an　arc　fault　experimental　setup　that　adheres　to　standard　requirements.　The　system　collects　multimodal　data,　including　diverse　types　of　arc　fault　signals　and　fire　images.　By　analyzing　this　data,　the　mechanisms　through　which　arc　faults　initiate　and　influence　fire　evolution　are　elucidated.　A　specialized　tool　is　developed　to　annotate　key　timestamps　in　multimodal　data　and　construct　a　high-quality　dataset　for　model　development.　To　address　the　complex　application　scenarios　of　arc　fault　fires,　the　fire　prediction　multimodal　Fusion　Transformer　(FireMultiFormer)　model　is　proposed.　The　model　integrates　fault　signals　and　fire　images　to　improve　the　accuracy　of　arc　fault　fire　predictions.　It　uses　the　patch　time-series　Transformer　(PatchTST)　and　the　Vision　Transformer　(ViT)　to　extract　features　from　current　and　image　sequences,　respectively,　and　employs　a　Fusion　Transformer　to　fuse　these　multimodal　features.　This　approach　leverages　data　complementarity　to　improve　the　understanding　and　discrimination　of　fire　states　caused　by　arc　faults.　Experimental　results　show　that　the　FireMultiFormer　model　achieves　high　accuracy　and　stability　across　various　datasets.　Case　studies　validate　the　model＇s　ability　to　recognize　and　learn　different　fire　states　effectively.　Ablation　studies　elucidate　the　impacts　of　model　configurations　and　sample　parameters　on　performance.　Compared　to　single-modal　and　alternative　methods,　the　multimodal　model　significantly　improves　performance　in　fire　prediction.　Furthermore,　the　proposed　model　enhances　the　performance　of　conventional　arc　fault　detection　methods　by　integrating　image　data.