In today’s complex project management landscape, effective risk management and resource allocation are critical to ensuring project success. Traditional approaches often rely on subjective assessments and static tools that fail to adapt to dynamic project environments. The advent of data-driven decision-making has revolutionized these processes by integrating advanced analytics, artificial intelligence (AI), and machine learning (ML) to provide actionable insights and predictive capabilities. This article explores how data-driven methodologies enhance risk identification, assessment, and mitigation, while simultaneously optimizing resource allocation to align with project objectives. By leveraging real-time data from multiple sources, such as historical project performance metrics, economic trends, and stakeholder feedback, project managers can make informed decisions that reduce uncertainties and maximize resource utilization. Predictive analytics tools enable the anticipation of potential risks and their impacts, allowing for proactive contingency planning. Furthermore, AI-driven algorithms facilitate resource optimization by analysing project timelines, budgets, and workforce availability to recommend efficient allocation strategies. Despite its transformative potential, the integration of data-driven decision-making faces challenges, including data quality issues, lack of standardization, and resistance to adoption. This article discusses strategies to overcome these barriers, emphasizing the importance of organizational culture, robust data governance frameworks, and scalable analytics tools. Case studies across various industries illustrate the tangible benefits of adopting data-driven approaches in managing project risks and resources. By narrowing the focus to practical applications and best practices, this article highlights the pivotal role of data-driven decision-making in driving project success, fostering agility, and enhancing organizational resilience in a rapidly evolving business environment.