AI, Project Management; Project Scheduling Optimization; Resource Allocation; Machine Learning; Genetic Algorithms; Neural Networks; Reinforcement Learning; Swarm Intelligence; Multi-Agent Systems; Performance Metrics; Case Studies; Scheduling Tools; Construction Projects; IT Resource Planning; Automation in Project Management Project scheduling and resource allocation are critical components of successful project management, especially in complex and dynamic environments. Traditional approaches, such as the Critical Path Method (CPM) and linear programming, often fall short in handling uncertainty, dynamic changes, and the increasing volume of project data. In recent years, artificial intelligence (AI) techniques have emerged as powerful tools to enhance scheduling accuracy, optimize resource utilization, and support real-time decision-making. This review paper explores various AI methodologies—including machine learning, genetic algorithms, neural networks, reinforcement learning, swarm intelligence, and multi-agent systems—and their applications in project scheduling and resource allocation. Through comparative analysis and industry case studies across sectors such as construction, IT, and manufacturing, the paper highlights performance benefits in terms of accuracy, cost savings, and time efficiency. Additionally, the study discusses integration with commercial project management tools and examines challenges in data quality, system compatibility, and ethical AI deployment. The review concludes by identifying current trends and future research directions that can further advance AI-driven project management practices.