Safety　and　security　of　humans　is　an　important　concern　in　every　aspect.　With　the　advancement　in　engineering,　sciences,　and　technology　(unfortunately)　new　methods　to　harm　humans　have　also　been　introduced.　At　the　same　time,　scientists　are　paying　attention　to　the　security　aspects　by　developing　new　software　and　hardware　gadgets.　In　comparison　to　the　system　level　security,　the　safety/security　of　human　beings　is　more　important.　Suicide　bombing　is　one　such　nuisance　that　is　still　an　open　challenge　for　the　world　to　detect　before　it　is　triggered.　This　work　deals　with　the　identification　of　a　suicide　bomber　using　a　3D　depth　camera　and　machine　learning　techniques.　This　work　utilizes　the　skeletal　data　provided　by　the　3D　depth　camera　to　identify　a　bomber　wearing　a　suicide　jacket.　The　prediction　is　based　on　real-time　3D　posture　data　of　the　body　joints　obtained　through　the　depth　camera.　Using　a　comprehensive　experimental　design,　a　dataset　is　created　consisting　of　20　joints　information　obtained　from　120　participants.　The　dataset　records　this　for　each　of　the　participants　with　and　without　wearing　a　suicide　jacket.　Experiments　are　performed　with　the　suicide　jacket　bearing　10-　to　20-kg　weight.　Simulations　are　performed　using　3D　spatial　features　of　the　participants＇　body　in　four　ways:　full　body　joints　(20　joints),　upper-half　of　the　body　(above　the　spine　base　of　the　skeleton),　20　joints　with　15　frames,　and　20　joints　with　20　frames.　It　is　observed　that　15　to　20　frames　are　sufficient　to　identify　a　suspected　suicide　bomber.　The　proposed　framework　utilize　four　classifiers　to　identify　vulnerability　of　a　subject　to　be　a　suicide　bomber.　Results　show　that　the　proposed　framework　is　capable　of　identifying　a　suicide　bomber　with　an　average　accuracy　of　92.30%.