This research explrores the application of deep learning techniques to improve prediction accuracy of project cost and time at completion as a response to traditional methods limitations due to growing complexity of modern projects. An accurate forecast is key for effective project management, reducing the risk of budget overruns and schedule delays. The proposed multilayer perceptron (MLP)-based deep learning model is compared against benchmark methods which are based on high-performing machine learning model (XGBoost) and traditional Earned Value Management (EVM). This study uses two datasets: Dynamic Scheduling Library (DSLIB), containing 181 projects, and 8 additional projects from Project Portfolio Dataset (Australian) for more diverse context.