Accurate cost estimates for IT projects are critical for effective software development planning, budgeting, and risk management.  Traditional cost assessment strategies, such as expert opinion, parametric models, and historical analogies, frequently fail to yield trustworthy conclusions, particularly in dynamic situations marked by rapid technological change and little historical data. This study describes the creation of a machine learning-based model intended to improve the accuracy and reliability of IT project cost estimation. The suggested approach makes use of advanced machine learning algorithms such as Random Forest and Gradient Boosting, which have been trained on datasets that include both real-world and synthetically created project data.  Key project variables, including project size (LOC), team size, development duration, project complexity, and development technique, are used as predictive features.  Synthetic data creation techniques are used to solve data scarcity and augment the training dataset, ensuring a greater coverage of potential project scenarios. Machine learning models outperform traditional methods in prediction accuracy, with reduced mean absolute error (MAE) and greater coefficient of determination (R²). Feature importance analysis reveals the most important aspects influencing project costs, giving vital information for project managers and decision-makers. This study advances intelligent decision support systems for IT project management and demonstrates the potential of machine learning to improve cost prediction procedures, particularly in situations when historical data is few or insufficient.