<![CDATA[Machine learning (ML) is transforming various industries, including project management. However, a significant gap remains in understanding how ML impacts key areas of project management. This lack of clarity may prevent organisations from fully harnessing ML to improve project outcomes, enhance efficiency, and optimise resource allocation. Bridging this gap is crucial to unlocking ML's potential for driving more effective project management practices. This study examines the role of machine learning in project management, with a focus on forecasting, risk management, and resource optimization. It identifies popular algorithms like SVM, ANN, and RF, while also exploring the potential of LSTM and CNN for handling sequential data. The study observes a growing trend towards hybrid models that combine traditional and advanced techniques, although simpler models such as DT and regression remain valued for their reliability. It highlights ML's benefits in boosting project efficiency and addresses challenges related to data requirements and algorithm complexity, offering recommendations for adopting scalable and interpretable models.]]>
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