In　this　paper,　we　enhanced　change　point　detection　in　skew　normal　distribution　models　by　integrating　the　EM　algorithm＇s　Q-function　with　the　modified　information　criterion　(MIC).　The　new　QMIC　framework　improves　sensitivity　and　accuracy　in　detecting　changes,　outperforming　the　modified　information　criterion　(MIC)　and　the　traditional　Bayesian　information　criterion　(BIC).　Due　to　the　complexity　of　deriving　analytic　asymptotic　distributions,　bootstrap　simulations　were　used　to　determine　critical　values　at　various　significance　levels.　Extensive　simulations　demonstrate　that　QMIC　offers　superior　detection　capabilities.　We　applied　the　QMIC　method　to　two　stock　market　datasets,　successfully　identifying　multiple　change　points,　and　highlighting　its　effectiveness　for　real-　world　financial　data　analysis.