<p>The growing complexity of software systems and the increasing demand for global software development necessitate innovative approaches to enhance project management, quality assurance, and risk mitigation. In this study, Artificial Intelligence (AI) and Machine Learning (ML) techniques are examined for overcoming problems in software project management, notably effort estimation, scheduling, resource allocation, risk management, and defect prediction. By systematically reviewing the literature, we show that AI/ML models like Support Vector Machines, neural networks, and ensemble learning can enhance estimation accuracy, maximize resource utilization, and reduce risks. Furthermore, the practical benefits and challenges of implementing an AI/ML system into a real-world system are discussed using real-world case studies, which include data quality and integration issues, and the interpretability of the model. In addition, advanced models, such as graph convolutional networks and deep neural networks, hold great promise as a defect prediction and bug severity classifier. The focus of this research is to leverage the transformative capabilities of AI/ML toward defect-free, efficient, and customer-centric software development. Finally, it suggests future research interests, including integrating explanation model AI, managing data in a better way, and implementing the scalable hybrid approach to meet the newer needs of the industry.</p>