Machine　learning　and　data　mining　are　being　used　in　different　fields　like　data　analysis,　prediction,　image　processing,　etc.,　and　particularly　in　healthcare.　Over　the　past　decade,　several　types　of　research　have　been　carried　out　focusing　on　machine　learning　and　data　mining　application　to　generate　intuitions　from　historical　data　and　make　predictions　about　the　results.　Machine　learning　algorithms　play　a　vital　role　in　improving　healthcare　systems　due　to　continuous　research　in　machine　learning　applications.　Several　researchers　have　used　algorithms　of　machine　learning　to　develop　systems　for　decision　support,　analyze　clinical　aspects,　use　historical　data　to　extract　useful　information,　make　future　predictions　and　categorize　diseases,　etc.　to　help　physicians　make　better　decisions.　In　this　study,　we　used　an　ensemble　modeling　voting　technique　for　the　classification　of　the　birth　dataset.　Ensemble　models　combine　individual　machine　learning　algorithms　to　improve　the　accuracy　by　predicting　from　the　combined　output　of　the　base　classifiers.　Gradient　boosting　classifier　(GBC),　random　forest　(RF),　bagging　classifier　(BC),　and　extra　trees　classifier　(ETC)　were　used　as　base　learners　for　making　a　voting　ensemble　model　for　the　classification　of　the　birth　dataset.　The　results　produced　have　shown　that　the　voting　classifier　of　support　vector　machine　(SVM),　random　forest　(RF),　extra　trees　classifier,　and　bagging　classifier　has　given　the　best　results　with　the　proportion　of　94.78%,　gradient　boosting　classifier　has　84.39%　accuracy,　the　random　forest　has　94.26%　accuracy,　extra　trees　classifier　have　94.02%　accuracy　and　bagging　classifier　has　93.65%　accuracy.　The　accuracy　achieved　by　ensemble　modeling　is　far　higher　than　the　machine　learning　algorithms.　Ensemble　models　increase　the　accuracy　of　machine　learning　algorithms　by　reducing　variance　and　classification　errors.　The　development　of　such　a　system　will　not　only　help　health　organizations　to　take　effective　measures　to　improve　the　maternal　health　assessment　process　but　will　also　open　the　doors　for　interdisciplinary　research　in　two　different　fields　in　the　region.