Due to the expansion of industrial operations globally in recent years, waste output has risen. So these wastes must be reduced by recycling and reusing to achieve environmentally friendly buildings and find various alternative materials in critical cases. The statistical indicators are used as practical study including Multiple linear regression (MLR) and artificial neural network (ANN) models. The study's goals were to assess the effectiveness of granite waste (GW) as a replacement for cement, sand, plastic, and binder in specific building applications and the relationships between MLR and ANN approaches. Results show the efficiency of adding granite waste to some construction stages and replacing it with cement in the mixture and examining its strength, it gave excellent results in addition to good results for its use as a binder in cement mortar, while the results were weak when used as a substitute for sand and plastic in insulator because it's classified as fine sand, Therefore, it cannot be used as a substitute for sand in the construction. The statistical models give an effective indicator to use GW as an alternative material ( binder and cement) based on the coefficient of correlation (R2) for the two models MLR and ANN equal to 83.4 % and 80 % respectively.