The　band　gap　is　a　key　parameter　in　semiconductor　materials　that　is　essential　for　advancing　optoelectronic　device　development.　Accurately　predicting　band　gaps　of　materials　at　low　cost　is　a　significant　challenge　in　materials　science.　Although　many　machine　learning　(ML)　models　for　band　gap　prediction　already　exist,　they　often　suffer　from　low　interpretability　and　lack　theoretical　support　from　a　physical　perspective.　In　this　study,　we　address　these　challenges　by　using　a　combination　of　traditional　ML　algorithms　and　the　‘white-box’　sure　independence　screening　and　sparsifying　operator　(SISSO)　approach.　Specifically,　we　enhance　the　interpretability　and　accuracy　of　band　gap　predictions　for　binary　semiconductors　by　integrating　the　importance　rankings　of　support　vector　regression　(SVR),　random　forests　(RF),　and　gradient　boosting　decision　trees　(GBDT)　with　SISSO　models.　Our　model　uses　only　the　intrinsic　features　of　the　constituent　elements　and　their　band　gaps　calculated　using　the　Perdew–Burke–Ernzerhof　method,　significantly　reducing　computational　demands.　We　have　applied　our　model　to　predict　the　band　gaps　of　1208　theoretically　stable　binary　compounds.　Importantly,　the　model　highlights　the　critical　role　of　electronegativity　in　determining　material　band　gaps.　This　insight　not　only　enriches　our　understanding　of　the　physical　principles　underlying　band　gap　prediction　but　also　underscores　the　potential　of　our　approach　in　guiding　the　synthesis　of　new　and　valuable　semiconductor　materials.　©　2024　by　the　authors.