In today’s dynamic business landscape, project risk management is crucial for ensuring the successful executionof complex initiatives. Traditional risk management frameworks rely on historical data, expert judgment, anddeterministic models, which often lack the adaptability required to address rapidly evolving project environments.Integrating artificial intelligence (AI)-driven predictive analytics into project risk management enhances decisionmaking by leveraging advanced data analysis, machine learning algorithms, and real-time risk assessment. AIenables organizations to proactively identify potential risks, quantify their impact, and recommend optimalmitigation strategies. By analysing structured and unstructured data from diverse sources, AI-driven predictiveanalytics provides deeper insights into risk patterns, allowing project managers to shift from reactive to proactivedecision-making. This paper explores the integration of AI-driven predictive analytics in project risk management,focusing on its ability to optimize risk identification, assessment, and response strategies. It examines key AImethodologies, including machine learning models, natural language processing (NLP), and reinforcementlearning, that enhance risk prediction accuracy. Furthermore, it discusses the challenges of AI adoption, such asdata reliability, model interpretability, and integration with existing project management tools. A comparativeanalysis of AI-enhanced risk management versus conventional approaches demonstrates its effectiveness inimproving project performance efficiency, reducing cost overruns, and mitigating schedule delays. The studyconcludes with future directions for AI-driven project risk management, emphasizing the need for hybrid AIhuman decision-making models to enhance strategic project execution.