The increasing unpredictability of global supply chains necessitate advanced technological solutions for disruption mitigation. It explored the integration of Artificial Intelligence (AI) and Machine Learning (ML) in project management to enhance supply chain resilience. AI-driven risk identification and forecasting enable organizations to anticipate disruptions and proactively manage risks, while machine learning models optimize supply chain operations through predictive analytics and anomaly detection. The application of AI in decision-making and real-time supply chain adaptation further enhances agility, leveraging scenario planning, digital twins, and AI-powered automation in logistics. Additionally, the convergence of blockchain with AI and ML has introduced unprecedented transparency in supply chain operations. Blockchain-integrated AI enhances real-time tracking, while smart contracts automate compliance, ensuring greater accountability across global supply networks. However, despite these advancements, significant challenges persist. Issues such as data quality and bias in AI-based forecasting, high implementation costs, cybersecurity risks, ethical concerns, and resistance to AI adoption hinder widespread deployment