This study examines how predictive analytics integrated into procurement software influences data-driven decision-making for smarter and more resilient supply chain management in the United States. The study was premised on five research objectives and five research questions. Anchored on the underpinnings of Resource-Based View Theory, the study uses Survey Research Method with questionnaire as the instrument of data collection. The population of the study comprises procurement and supply chain professionals working in mid- to large-sized enterprises across major industrial sectors in the United States, including manufacturing, retail, healthcare, and logistics. Firms included in the population are characterized by their usage of enterprise procurement software such as SAP Ariba, Coupa, Oracle Procurement Cloud, and similar systems with predictive analytic capabilities. Using Cochran’s (1977) formula, a sample of 412 respondents was used for this study while a multi-stage sampling technique was adopted to select the final sample. Finding revealed that predictive analytics are increasingly integrated into procurement operations across various sectors in the United States. With an average mean of 4.1, respondents strongly affirmed that historical data and predictive tools have been embedded into workflows for forecasting needs, optimizing procurement cycles, and informing demand planning. Finding further revealed that substantial proportion of respondents (mean = 4.3) highlighted measurable procurement gains from using predictive analytics, such as improved accuracy, cost savings, and enhanced negotiation capabilities while outdated IT systems, insufficient skilled personnel, and budget limitation were revealed as the barriers to predictive analytics adoption. The study recommended that organizations across the United States should prioritize the strategic integration of predictive analytics within their procurement systems to improve efficiency, risk management, and supply chain agility. Procurement professionals should also be upskilled in data analytics, and firms must invest in robust digital infrastructures that support real-time, high-quality data collection and analysis.