Accurate price prediction is critical for ecommerce platforms, particularly for feature rich products like laptops. This study evaluates ten machine learning (ML) algorithms; six baseline regression models (Linear Regression, Ridge, Lasso, Support Vector Regression, Decision Tree, KNearest Neighbors) and four ensemble models (Random Forest, Gradient Boosting, AdaBoost, XGBoost), using a Kaggle dataset of 1,300 laptop entries. Features include processor type, RAM, storage, screen resolution, and brand. Performance was assessed via Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R², with GridSearchCV for hyperparameter tuning. Ensemble models, notably XGBoost and Gradient Boosting, outperformed baseline regressors, achieving RMSE ≈ 49,885 and R² ≈ 0.89 on the validation set. Feature importance analysis identified RAM, CPU, and brand as key price drivers. This system supports real time pricing in ecommerce, balancing accuracy and computational efficiency.