Motorcycles　and　motorcyclists　have　a　variety　of　attributes　that　have　been　found　to　be　a　potential　contributor　to　the　high　liability　of　vulnerable　road　users　(VRUs).　Vulnerable　Road　Users　(VRUs)　that　include　pedestrians,　bicyclists,　cycle-rickshaw　occupants,　and　motorcyclists　constitute　by　far　the　highest　share　of　road　traffic　accidents　in　developing　countries.　Motorized　three-wheeled　Rickshaws　(3W-MR)　is　a　popular　public　transport　mode　in　almost　all　Pakistani　cities　and　is　used　primarily　for　short　trips　to　carry　passengers　and　small-scale　goods　movement.　Despite　being　an　important　mode　of　public　transport　in　the　developing　world,　little　work　has　been　done　to　understand　the　factors　affecting　the　injury　severity　of　three-wheeled　motorized　vehicles.　Crash　injury　severity　prediction　is　a　promising　research　target　in　traffic　safety.　Traditional　statistical　models　have　underlying　assumptions　and　predefined　associations,　which　can　yield　misleading　results　if　flouted.　Machine　learning(ML)　is　an　emerging　non-parametric　method　that　can　effectively　capture　the　non-linear　effects　of　both　continuous　and　discrete　variables　without　prior　assumptions　and　achieve　better　prediction　accuracy.　This　research　analyzed　injury　severity　of　three-wheeled　motorized　rickshaws　(3W-MR)　using　various　machine　learning-based　identification　algorithms,　i.e.,　Decision　jungle　(DJ),　Random　Forest　(RF),　and　Decision　Tree　(DT).　Three　years　of　crash　data　(from　2017　to　2019)　was　collected　from　Provincial　Emergency　Response　Service　RESCUE　1122　for　Rawalpindi　city,　Pakistan.　A　total　of　2,743　3W-MR　crashes　were　reported　during　the　study　period　that　resulted　in　258　fatalities.　The　predictive　performance　of　proposed　ML　models　was　assessed　using　several　evaluation　metrics　such　as　overall　accuracy,　macro-average　precision,　macro-average　recall,　and　geometric　means　of　individual　class　accuracies.　Results　revealed　that　DJ　with　an　overall　accuracy　of　83.7　%　outperformed　the　DT　and　RF-based　on　a　stratified　10-fold　cross-validation　approach.　Finally,　Spearman　correlation　analysis　showed　that　factors　such　as　the　lighting　condition,　crashes　involving　young　drivers　(aged　20?30　years),　facilities　with　high-speed　limits　(over　60　mph),　weekday,　off-peak,　and　shiny　weather　conditions　were　more　likely　to　worsen　injury　severity　of　3W-MR　crashes.　The　outcomes　of　this　study　could　provide　necessary　and　essential　guidance　to　road　safety　agencies,　particularly　in　the　study　area,　for　proactive　implementation　of　appropriate　countermeasures　to　curb　road　safety　issues　pertaining　to　three-wheeled　motorized　vehicles.