The　field　of　materials　science　has　experienced　a　transformative　shift　with　the　emergence　of　high-entropy　materials　(HEMs),　which　possess　a　unique　combination　of　properties　that　traditional　single-phase　materials　lack.　Among　these,　high-entropy　nitrides　(HENs)　stand　out　for　their　exceptional　mechanical　strength,　thermal　stability,　and　resistance　to　extreme　environments,　making　them　highly　sought　after　for　applications　in　aerospace,　defense,　and　energy　sectors.　Central　to　the　design　of　these　materials　is　their　entropy　forming　ability　(EFA),　a　measure　of　a　material＇s　propensity　to　form　a　single-phase,　disordered　structure.　This　study　introduces　the　application　of　the　sure　independence　screening　and　sparsifying　operator　(SISSO),　a　machine　learning　technique,　to　predict　the　EFA　of　HEN　ceramics.　By　utilizing　a　rich　dataset　curated　from　theoretical　computational　data,　SISSO　has　been　trained　to　identify　the　most　critical　features　contributing　to　EFA.　The　model＇s　strong　interpretability　allows　for　the　extraction　of　complex　mathematical　expressions,　providing　deep　insights　into　the　material＇s　composition　and　its　impact　on　EFA.　The　predictive　performance　of　the　SISSO　model　is　meticulously　validated　against　theoretical　benchmarks　and　compared　with　other　machine　learning　methodologies,　demonstrating　its　superior　accuracy　and　reliability.　This　research　not　only　contributes　to　the　growing　body　of　knowledge　on　HEMs　but　also　paves　the　way　for　the　efficient　discovery　and　development　of　new　HEN　materials　with　tailored　properties　for　advanced　technological　applications.