The　application　of　machine　learning　algorithms　in　studying　injury　assessment　methods　based　on　data　analysis　has　recently　provided　a　new　research　insight　for　sports　injury　prevention.　However,　the　data　used　in　these　studies　are　primarily　multi-source　and　multimodal　(i.e.,　longitudinal　repeated-measures　data　and　cross-sectional　data),　resulting　in　the　models　not　fully　utilising　the　information　in　the　data　to　reveal　specific　injury　risk　patterns.　Therefore,　this　study　proposed　an　injury　risk　prediction　model　based　on　a　multi-modal　strategy　and　machine　learning　algorithms　to　handle　multi-source　data　better　and　predict　injury　risk.　This　study　retrospectively　analysed　the　routine　monitoring　data　of　sixteen　young　female　basketball　players.　These　data　included　training　load,　perceived　well-being　status,　physiological　response,　physical　performance　and　lower　extremity　non-contact　injury　registration.　This　study　partitions　the　original　dataset　based　on　the　frequency　of　data　collection.　Extreme　gradient　boosting　(XGBoost)　was　used　to　construct　unimodal　submodels　to　obtain　decision　scores　for　each　category　of　indicators.　Ultimately,　the　decision　scores　from　each　submodel　were　fused　using　the　random　forest　(RF)　to　generate　a　lower　extremity　non-contact　injury　risk　prediction　model　at　the　decision-level.　The　10-fold　cross-validation　results　showed　that　the　fusion　model　was　effective　in　classifying　non-injured　(mean　Precision:　0.9932,　mean　Recall:　0.9976,　mean　F2-score:　0.9967),　minimal　lower　extremity　non-contact　injuries　risk　(mean　Precision:　0.9317,　mean　Recall:　0.9167,　mean　F2-score:　0.9171),　and　mild　lower　extremity　non-contact　injuries　risk　(mean　Precision:　0.9000,　mean　Recall:　0.9000,　mean　F2-score:　0.9000).　The　model　performed　significantly　more　optimal　than　the　submodel.　Comparing　the　fusion　model　proposed　with　a　traditional　data　integration　scheme,　the　average　Precision　and　Recall　improved　by　8.2　and　20.3%,　respectively.　The　decision　curves　analysis　showed　that　the　proposed　fusion　model　provided　a　higher　net　benefit　to　athletes　with　potential　lower　extremity　non-contact　injury　risk.　The　validity,　feasibility　and　practicality　of　the　proposed　model　have　been　confirmed.　In　addition,　the　shapley　additive　explanation　(SHAP)　and　network　visualisation　revealed　differences　in　lower　extremity　non-contact　injury　risk　patterns　across　severity　levels.　The　model　proposed　in　this　study　provided　a　fresh　perspective　on　injury　prevention　in　future　research.