This paper presents a comprehensive review of Data-Driven Prescriptive Analytics (DPSA) models and their role in enhancing operational efficiency across diverse industry sectors. By synthesizing existing frameworks, algorithms, and real-world implementations, the study identifies how DPSA leverages historical and real-time data to recommend actionable strategies, optimize decision-making, and allocate resources more effectively. Drawing from both conceptual and empirical literature published up to 2021—including advanced AI-driven analytics, cloud-based business intelligence systems, and predictive modeling—the review highlights the transformative power of DPSA in sectors such as manufacturing, energy, finance, and supply chain management. Special attention is given to barriers such as data silos, model interpretability, and integration challenges in small and medium enterprises (SMEs). The analysis also explores how innovations like natural language processing, machine learning, and real-time data governance systems are embedded into DPSA architectures to improve scalability and adaptability. Ultimately, this review contributes to the understanding of how prescriptive analytics can support sustainable business operations, agile response mechanisms, and competitive advantage in data-intensive environments.