Water　quality　prediction　methods　forecast　the　short-or　long-term　trends　of　its　changes,　providing　proactive　advice　for　preventing　and　controlling　water　pollution.　Existing　water　quality　prediction　methods　typically　fail　to　capture　water　quality＇s　nonlinear　characteristics　accurately　and　only　consider　historical　time　series　data.　However,　meteorology　and　other　factors　also　significantly　impact　water　quality　indicators.　Therefore,　considering　only　historical　data　of　water　quality　time　series　is　not　feasible.　To　solve　this　problem,　this　work　proposes　a　hybrid　water　quality　prediction　model　called　CMLIP,　which　integrates　ConvNeXt　V2,　Multimodal　bottleneck　transformer,　Low-rank　multimodal　fusion,　ITransformer,　and　PatchTST.　CMLIP　inputs　water　quality　time　series　and　meteorological　remotely　sensed　rainfall　images　into　a　multimodal　fusion　module　before　prediction.　Specifically,　CMLIP　integrates　the　model　of　ConvNeXt　V2　to　extract　image　features.　Its　multimodal　fusion　module　combines　a　multimodal　bottleneck　transformer　and　the　low-rank　multimodal　fusion　to　fuse　the　time　series　and　images.　Furthermore,　CMLIP　combines　iTransformer　and　PatchTST　to　form　an　improved　prediction　module　that　realizes　the　prediction　of　fused　features.　Experimental　results　with　real-life　water　quality　time　series　and　remotely　sensed　rainfall　images　demonstrate　that　CMLIP　when　fusing　meteorological　data,　achieves　an　average　improvement　of　17%　in　water　quality　forecasting　accuracy　compared　to　forecasts　using　only　water　quality　time　series.　Moreover,　CMLIP　outperforms　other　state-of-the-art　algorithms　in　both　data　fusion　and　prediction,　with　an　average　enhancement　of　6%　in　fusion　effectiveness　and　an　average　improvement　of　22%　in　prediction　accuracy.　©　2025　IEEE.