In this paper, we aim to develop a car price prediction model using data collected from an online sales platform. To accomplish the proposed objective, we applied the following approaches and techniques: (1) Collecting sales data from the online sales platform; (2) Exploratory analysis of data before and after data preprocessing; (3) Experimenting to find a suitable prediction model for the collected dataset. The novelty of this study lies in constructing a real-world dataset of pre-owned car prices collected directly from an online sales platform and in building a car price prediction model using an empirical approach combined with machine learning models. Unlike previous studies based on existing structured datasets, this study emphasizes the discovery of data-driven insights through exploratory analysis and the identification of key variables affecting car prices. At the same time, essential insights regarding car prices were obtained from the dataset. Experimental results show that the model using the XGBoost algorithm achieved an R2 of 0.776 for the default parameter case and an R2 of 0.779 for the optimized parameter case. These findings provide a practical solution for real-world car price prediction systems, allowing buyers and sellers to make more informed pricing decisions.