Although very crucial for decision-making within the automotive market, price prediction for cars in the developing countries is generally not taken into account by the most present-day available complex models. This paper will finally evaluate how effective machine learning algorithms are in predicting car prices from a set of data standard reflecting attributes of vehicles, including engine size, miles traveled by the vehicle, fuel efficiency, and sales history. Five regression models, namely, Multiple Linear Regression, Lasso Regression, Ridge Regression, Support Vector Regression (SVR), and XGBoost, were developed and validated using RMSE and R-squared evaluation metrics. The results indicate that linear models that include MLR and ridge regression outperform all the others since they yield the best RMSE (4.245) and R (0.926), thereby proving strength in modeling the linear relationships existing within the data. Conversely, an SVR performed poorly (RMSE: 12.113; R2: 0.394), possibly as a result of a malfunction in line trend modeling. Establishing multicollinearity in features like engine size, horsepower, and fuel efficiency makes feature selection important. Most importantly, as far as car price prediction is concerned, it has established linear regression-based models as superior, proving practically useful for anybody seeking dependable pricing tools—a buyer, seller, or industry stakeholder.