In Malaysia, the used car market frequently lacks transparency and fair pricing, making it difficult for buyers and sellers to make wise decisions. In-depth research on the creation of a machine learning-based pricing prediction model created exclusively for the Malaysian used car market is presented in this paper. The main goal of this project is to develop a reliable and open model that predicts used car pricing based on essential variables including age, condition, location, and the availability of comparable models on the market. Numerous techniques, like the neural network and regression model, are used to accomplish this purpose. The scope of the project also encompasses the identification of challenges and limitations in the current used car market in Malaysia, along with proposed solutions to improve transparency and fairness for all stakeholders. The methodology involves data collection, preprocessing, feature selection, model training, and evaluation. The results demonstrate that the developed model provides precise and transparent pricing information, empowering buyers and sellers to make informed decisions regarding their transactions. This project holds significant potential for enhancing transparency and fairness in the Malaysian used car market, while also serving as a valuable reference for similar initiatives in other countries and markets. © 2023 IEEE.