Machine learning (ML) has emerged as a transformative tool in healthcare, offering unprecedented opportunities to enhance efficiency, accuracy, and decision-making across various domains. One of the critical areas benefiting from this technological advancement is project management in healthcare delivery. Traditional approaches often struggle to accommodate the complexities and dynamic nature of healthcare processes, resulting in inefficiencies, delays, and increased costs. ML-driven predictive techniques address these challenges by leveraging large datasets to forecast project outcomes, optimize resource allocation, and mitigate risks. This paper explores the integration of machine learning into predictive project management for healthcare delivery improvement. It provides a comprehensive analysis of ML algorithms such as neural networks, decision trees, and ensemble methods that predict bottlenecks, resource shortages, and task delays. By examining real-world case studies, the research highlights the transformative impact of these techniques on patient outcomes, operational workflows, and cost reduction. For instance, predictive models have been successfully implemented to forecast patient admissions, optimize staffing, and streamline surgical schedules, showcasing the potential of ML in reducing operational inefficiencies. In addition to technical advancements, the paper discusses ethical and regulatory considerations critical to implementing ML solutions in healthcare project management. It emphasizes the importance of transparency, interpretability, and compliance with frameworks such as HIPAA and GDPR to ensure ethical adoption. The findings underscore the role of interdisciplinary collaboration in deploying ML-driven project management tools that align with healthcare goals, ensuring improved service delivery and patient care. Future directions include expanding research on dynamic models that adapt to real-time data changes and exploring the broader implications of ML on healthcare project management.