Precise price forecasting can aid manufacturers in establishing competitive pricing and help consumers make informed decisions when purchasing laptops. The objective of this research is to create a laptop price projection model using Gradient Boost, a robust machine learning algorithm recognized for its exceptional performance in regression assignments. In general, 1302 embedded parameters with 15 variables, together with various technical info such as CPU version, RAM ability, garage length, display dimensions, and logo, made up the dataset applied for this evaluation. The Gradient Boost method was used to construct the forecasting version due to its capability to reduce mistakes by amalgamating several vulnerable rookies right into an unmarried effective learner. The dataset turned into to begin with processed and divided into education units which were applied to educate the Gradient Boost model. The R2 score, a degree of consistency between projected values and actual values, was employed to evaluate the effectiveness of the version.