Enhancements　in　monitoring　and　computational　technology　have　facilitated　data　accessibility　and　utilization.　Machine　learning,　as　an　integral　component　of　the　realm　of　computational　technology,　is　renowned　for　its　universality　and　efficacy,　rendering　it　pervasive　across　various　domains.　Geotechnical　disaster　early　warning　systems　serve　as　a　crucial　safeguard　for　the　preservation　of　human　lives　and　assets.　Machine　learning　exhibits　the　capacity　to　meet　the　exigencies　of　prompt　and　precise　disaster　prediction,　prompting　substantial　interest　in　the　nexus　of　these　two　domains　in　recent　decades.　This　study　accentuates　the　deployment　of　machine　learning　in　addressing　geotechnical　engineering　disaster　prediction　issues　through　an　examination　of　four　types　of　engineering-specialized　research　articles　spanning　the　period　2009　to　2024.　The　study　elucidates　the　evolution　and　significance　of　machine　learning　within　the　domain　of　geotechnical　engineering　disaster　prediction,　with　an　emphasis　on　data　analytics　and　modeling.　Addressing　the　lacunae　in　existing　literature,　a　user-friendly　front-end　graphical　interface,　integrated　with　machine　learning　algorithms,　is　devised　to　better　cater　to　the　requisites　of　engineering　professionals.　Furthermore,　this　research　delves　into　a　critical　analysis　of　the　prevalent　research　limitations　and　puts　forth　prospective　investigational　avenues　from　an　applied　standpoint.