To　improve　the　accuracy　of　demand　forecasting　for　new　electronic　products,　especially　in　scenarios　with　limited　historical　data,　a　novel　forecasting　model　was　proposed　in　this　study　which　integrated　K-means　based　on　Euclidian　distance,　Multi-layer　perceptron　algorithm,　and　Quantile　Regression　with　Gradient　Boosted　Trees　(KEM-QRGBT).　The　model　also　incorporated　grid　search　with　K-fold　cross-validation　to　enable　the　adaptive　selection　of　the　optimal　parameters　for　product　data.　Additionally,　the　KEM-QRGBT　model,　which　can　balance　the　intricacies　of　learning　parameter　patterns　with　its　ability　to　quantify　demand　uncertainty,　exhibited　proficiency　in　quantifying　the　uncertainty　inherent　in　demand　forecasting.　Using　a　case　study　from　a　manufacturing　enterprise　in　Turkey,　the　effectiveness　of　the　model　was　validated.　Results　demonstrate　that,　for　new　electronic　products　with　limited　historical　data,　the　KEM-QRGBT　model　with　adaptive　parameter　selection　improves　demand　forecasting　accuracy,　outperforming　benchmark　methods,　and　other　machine　learning　models.　The　proposed　algorithm　provides　a　strong　evidence　for　the　demand　forecasting　of　new　electronic　products,　particularly　in　cases　where　historical　data　is　limited.　©　2023　School　of　Science,　IHU.　All　Rights　Reserved.