Football, arguably the most popular sport in the world, has become much more than just a sport, it is a multibillion-dollar industry with its center in Europe. Every year millions of euros are spent in transfer window to buy and sell players and a common theme that has been seen is players not living up to the price the clubs paid for them. This research aims to predict football players’ market values using machine learning techniques. Departing from traditional methods that broadly categorize players into positions like Goalkeeper, Defender, Midfielder, and Forward, this study provides a more nuanced approach by classifying players into specific roles such as Center-back, Full-back, Defensive Midfielder, Attacking Midfielder, and Winger. By incorporating performance metrics tailored to each position and weighing the performance indicators based on the relevance to that specific position, the research aims to provide a robust method to predict players’ market value within a negotiation tolerance range. Using data from the past three seasons, including detailed player performance statistics and contractual details, models were developed to assist clubs in making data-driven transfer decisions. Machine learning algorithms, including Random Forest Regressor and Light GBM, were utilized, with RMSE and R2 Score as evaluation metrics. Both algorithms demonstrated robust performance, with some positional models predicting market values within an acceptable error range of 3–12 million, enabling clubs to negotiate transfer fees with greater precision based on empirical evidence of player performance.