Generally,　the　emergence　of　Internet　of　Things　enabled　applications　inspired　the　world　during　the　last　few　years,　providing　state-of-the-art　and　novel-based　solutions　for　different　problems.　This　evolutionary　field　is　mainly　lead　by　wireless　sensor　network,　radio　frequency　identification,　and　smart　mobile　technologies.　Among　others,　the　IoT　plays　a　key　role　in　the　form　of　smart　medical　devices　and　wearables,　with　the　ability　to　collect　varied　and　longitudinal　patient-generated　health　data,　and　at　the　same　time　also　offering　preliminary　diagnosis　options.　In　terms　of　efforts　made　for　helping　the　patients　using　IoT-based　solutions,　experts　exploit　capabilities　of　the　machine　learning　algorithms　to　provide　efficient　solutions　in　hemorrhage　diagnosis.　To　reduce　the　death　rates　and　propose　accurate　treatment,　this　paper　presents　a　smart　IoT-based　application　using　machine　learning　algorithms　for　the　human　brain　hemorrhage　diagnosis.　Based　on　the　computerized　tomography　scan　images　for　intracranial　dataset,　the　support　vector　machine　and　feedforward　neural　network　have　been　applied　for　the　classification　purposes.　Overall,　classification　results　of　80.67%　and　86.7%　are　calculated　for　the　support　vector　machine　and　feedforward　neural　network,　respectively.　It　is　concluded　from　the　resultant　analysis　that　the　feedforward　neural　network　outperforms　in　classifying　intracranial　images.　The　output　generated　from　the　classification　tool　gives　information　about　the　type　of　brain　hemorrhage　that　ultimately　helps　in　validating　expert＇s　diagnosis　and　is　treated　as　a　learning　tool　for　trainee　radiologists　to　minimize　the　errors　in　the　available　systems.　©　2020　Hang　Chen　et　al.