Accurate cost forecasting is vital in construction project management, where budget overruns and delays can have significant impacts. Traditional methods like Earned Value Management (EVM) are widely used, but they rely on static, linear assumptions that often fail to capture the complexities of real-world projects. This thesis explores the potential of machine learning (ML) algorithms to improve cost forecasting by addressing the limitations of EVM and offering more dynamic, data-driven predictions during project execution. A comparative analysis of six machine learning models—XGBoost, Extremely Randomized Trees, Random Forest, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbors (KNN)—was conducted using a dataset of 90 real-world construction projects, selected from 181 initial projects.