Simple　Summary:　Cancer　is　a　deadly　disease　that　needs　to　be　diagnose　at　early　stage　to　increase　patient　survival　rate.　Multi-organ　(such　as　breast,　brain,　lung,　and　skin)　cancer　detection,　segmentation　and　classification　manually　using　medical　imaging　is　time　consuming　and　required　high　expertise.　In　this　study,　we　summarize　existing　deep　learning　segmentation　and　classification　methods　for　multi-organ　cancer　diagnosis　and　provide　future　challenges　with　possible　solutions.　This　review　may　benefit　researchers　to　design　new　robust　approaches　that　could　be　useful　for　the　medical　specialists　as　a　second　view.　Thus　far,　the　most　common　cause　of　death　in　the　world　is　cancer.　It　consists　of　abnormally　expanding　areas　that　are　threatening　to　human　survival.　Hence,　the　timely　detection　of　cancer　is　important　to　expanding　the　survival　rate　of　patients.　In　this　survey,　we　analyze　the　state-of-the-art　approaches　for　multi-organ　cancer　detection,　segmentation,　and　classification.　This　article　promptly　reviews　the　present-day　works　in　the　breast,　brain,　lung,　and　skin　cancer　domain.　Afterwards,　we　analytically　compared　the　existing　approaches　to　provide　insight　into　the　ongoing　trends　and　future　challenges.　This　review　also　provides　an　objective　description　of　widely　employed　imaging　techniques,　imaging　modality,　gold　standard　database,　and　related　literature　on　each　cancer　in　2016-2021.　The　main　goal　is　to　systematically　examine　the　cancer　diagnosis　systems　for　multi-organs　of　the　human　body　as　mentioned.　Our　critical　survey　analysis　reveals　that　greater　than　70%　of　deep　learning　researchers　attain　promising　results　with　CNN-based　approaches　for　the　early　diagnosis　of　multi-organ　cancer.　This　survey　includes　the　extensive　discussion　part　along　with　current　research　challenges,　possible　solutions,　and　prospects.　This　research　will　endow　novice　researchers　with　valuable　information　to　deepen　their　knowledge　and　also　provide　the　room　to　develop　new　robust　computer-aid　diagnosis　systems,　which　assist　health　professionals　in　bridging　the　gap　between　rapid　diagnosis　and　treatment　planning　for　cancer　patients.