Alzheimer＇s　disease　(AD)　is　a　degenerative　disorder　that　leads　to　progressive,　irreversible　cognitive　decline.　To　obtain　an　accurate　and　timely　diagnosis　and　detect　AD　at　an　early　stage,　numerous　approaches　based　on　convolutional　neural　networks　(CNNs)　using　neuroimaging　data　have　been　proposed.　Because　3D　CNNs　can　extract　more　spatial　discrimination　information　than　2D　CNNs,　they　have　emerged　as　a　promising　research　direction　in　the　diagnosis　of　AD.　The　aim　of　this　article　is　to　present　the　current　state　of　the　art　in　the　diagnosis　of　AD　using　3D　CNN　models　and　neuroimaging　modalities,　focusing　on　the　3D　CNN　architectures　and　classification　methods　used,　and　to　highlight　potential　future　research　topics.　To　give　the　reader　a　better　overview　of　the　content　mentioned　in　this　review,　we　briefly　introduce　the　commonly　used　imaging　datasets　and　the　fundamentals　of　CNN　architectures.　Then　we　carefully　analyzed　the　existing　studies　on　AD　diagnosis,　which　are　divided　into　two　levels　according　to　their　inputs:　3D　subject-level　CNNs　and　3D　patch-level　CNNs,　highlighting　their　contributions　and　significance　in　the　field.　In　addition,　this　review　discusses　the　key　findings　and　challenges　from　the　studies　and　highlights　the　lessons　learned　as　a　roadmap　for　future　research.　Finally,　we　summarize　the　paper　by　presenting　some　major　findings,　identifying　open　research　challenges,　and　pointing　out　future　research　directions.