Three-dimensional　CAD　reconstruction　is　a　long-standing　and　important　task　in　fields　such　as　industrial　manufacturing,　architecture,　medicine,　film　and　television,　research,　and　education.　Reconstructing　CAD　models　remains　a　persistent　challenge　in　machine　learning.　There　have　been　many　studies　on　deep　learning　in　the　field　of　3D　reconstruction.　In　recent　years,　with　the　release　of　CAD　datasets,　there　have　been　more　and　more　studies　on　3D　CAD　reconstruction　using　deep　learning.　With　the　continuous　deepening　of　research,　deep　learning　has　significantly　improved　the　performance　of　tasks　in　the　field　of　CAD　reconstruction.　However,　this　task　remains　challenging　due　to　data　scarcity　and　labeling　difficulties,　model　complexity,　and　lack　of　generality　and　adaptability.　This　paper　reviews　both　classic　and　recent　research　results　on　3D　CAD　reconstruction　tasks　based　on　deep　learning.　To　the　best　of　our　knowledge,　this　is　the　first　investigation　focusing　on　the　CAD　reconstruction　task　in　the　field　of　deep　learning.　Since　there　are　relatively　few　studies　related　to　3D　CAD　reconstruction,　we　also　investigate　the　reconstruction　and　generation　of　2D　CAD　sketches.　According　to　the　different　input　data,　we　divide　all　investigations　into　the　following　categories:　point　cloud　input　to　3D　CAD　models,　sketch　input　to　3D　CAD　models,　other　input　to　3D　CAD　models,　reconstruction　and　generation　of　2D　sketches,　characterization　of　CAD　data,　CAD　datasets,　and　related　evaluation　indicators.　Commonly　used　datasets　are　outlined　in　our　taxonomy.　We　provide　a　brief　overview　of　the　current　research　background,　challenges,　and　recent　results.　Finally,　future　research　directions　are　discussed.