In an era when projects are becoming more complicated and uncertain, the ability to accurately predict project outcomes and use resources efficiently is more important than ever. This article investigates how predictive analytics can completely transform project management, with a focus on how it can be used to predict outcomes and optimize resource allocation. We conducted numerous experiments with a Gradient Boosting Machine (GBM) model and t-Distributed Stochastic Neighbor Embedding (t-SNE) for feature engineering to demonstrate how predictive analytics can significantly improve the accuracy of project outcome predictions and resource efficiency. The findings show that it is not only easier to predict when a project will be late or over budget, but it is also much easier to make the best use of resources, saving money and increasing the likelihood of project success. Even though there are some drawbacks, such as bad data, the need for technical expertise, and changes in how organizations make data-driven decisions, there are clear advantages to using predictive analytics in project management. According to this article, predictive analytics is an important set of tools for modern project management because it aids in better planning, successful project completion, and knowledge acquisition. The conclusion emphasizes the importance of adopting this data-driven approach, as it has the potential to completely change the way project management is done and the results obtained.