This paper explores the transformative potential of Artificial Intelligence (AI) in project management, focusing on its role in decision-making and risk management. The integration of AI into project management is reshaping the landscape by enabling more efficient and effective project execution. AI technologies such as Machine Learning (ML) and Deep Learning (DL) are being leveraged to analyze vast amounts of data, identify patterns, and make data-driven decisions. These technologies help project managers predict project performance, optimize resource allocation, and develop proactive risk mitigation strategies. Machine Learning algorithms can analyze historical project data to identify trends and patterns, allowing for better decision-making in areas such as scheduling and resource allocation. Deep Learning, on the other hand, can process unstructured data such as project narratives and risk reports, extracting insights that inform more accurate risk assessments and scenario planning. AI also enables the creation of realistic simulations of project outcomes under various scenarios, allowing project managers to anticipate potential disruptions and develop contingency plans. In terms of risk management, AI can analyze vast datasets to identify potential risks early in the project lifecycle, prioritize them based on likelihood and impact, and suggest mitigation strategies. AI models can continuously learn and improve as new data becomes available, ensuring that project teams operate with the latest information and adapt their approaches as needed. AI also plays a crucial role in dynamic contingency planning, allowing for real-time adjustments to project plans based on current data. Despite its potential, the implementation of AI in project management is not without challenges. Issues such as data bias, the need for human oversight, and ethical considerations must be addressed to ensure responsible and effective integration of AI. The paper emphasizes the importance of high-quality data, human-AI collaboration, and continuous monitoring to ensure that AI is used ethically and effectively. By addressing these challenges, project managers can harness the power of AI to enhance decision-making, mitigate risks, and ultimately achieve project success. The paper concludes that AI has the potential to revolutionize project management, leading to more efficient, predictable, and successful projects in the future.This paper explores the transformative potential of Artificial Intelligence (AI) in project management, focusing on its role in decision-making and risk management. The integration of AI into project management is reshaping the landscape by enabling more efficient and effective project execution. AI technologies such as Machine Learning (ML) and Deep Learning (DL) are being leveraged to analyze vast amounts of data, identify patterns, and make data-driven decisions. These technologies help project managers predict project performance, optimize resource allocation, and develop proactive risk mitigation strategies. Machine Learning algorithms can analyze historical project data to identify trends and patterns, allowing for better decision-making in areas such as scheduling and resource allocation. Deep Learning, on the other hand, can process unstructured data such as project narratives and risk reports, extracting insights that inform more accurate risk assessments and scenario planning. AI also enables the creation of realistic simulations of project outcomes under various scenarios, allowing project managers to anticipate potential disruptions and develop contingency plans. In terms of risk management, AI can analyze vast datasets to identify potential risks early in the project lifecycle, prioritize them based on likelihood and impact, and suggest mitigation strategies. AI models can continuously learn and improve as new data becomes available, ensuring that project teams operate with the latest information and adapt their approaches as needed. AI also plays a crucial role in dynamic contingency planning, allowing for real-time adjustments to project plans based on current data. Despite its potential, the implementation of AI in project management is not without challenges. Issues such as data bias, the need for human oversight, and ethical considerations must be addressed to ensure responsible and effective integration of AI. The paper emphasizes the importance of high-quality data, human-AI collaboration, and continuous monitoring to ensure that AI is used ethically and effectively. By addressing these challenges, project managers can harness the power of AI to enhance decision-making, mitigate risks, and ultimately achieve project success. The paper concludes that AI has the potential to revolutionize project management, leading to more efficient, predictable, and successful projects in the future.