Long　non-coding　RNAs　(lncRNAs)　are　involved　in　almost　the　entire　cell　life　cycle　through　different　mechanisms　and　play　an　important　role　in　many　key　biological　processes.　Mutations　and　dysregulation　of　lncRNAs　have　been　implicated　in　many　complex　human　diseases.　Therefore,　identifying　the　relationship　between　lncRNAs　and　diseases　not　only　contributes　to　biologists＇　understanding　of　disease　mechanisms,　but　also　provides　new　ideas　and　solutions　for　disease　diagnosis,　treatment,　prognosis　and　prevention.　Since　the　existing　experimental　methods　for　predicting　lncRNA-disease　associations　(LDAs)　are　expensive　and　time　consuming,　machine　learning　methods　for　predicting　lncRNA-disease　associations　have　become　increasingly　popular　among　researchers.　In　this　review,　we　summarize　some　of　the　human　diseases　studied　by　LDAs　prediction　models,　association　and　similarity　features　of　LDAs　prediction,　performance　evaluation　methods　of　models　and　some　advanced　machine　learning　prediction　models　of　LDAs.　Finally,　we　discuss　the　potential　limitations　of　machine　learning-based　methods　for　LDAs　prediction　and　provide　some　ideas　for　designing　new　prediction　models.