The integration of advanced analytics in retail supply chains has transformed operational efficiency, demand forecasting, and risk mitigation. This study examines the impact of predictive analytics, machine learning, and AI-driven risk intelligence on supply chain performance. Using a mixed-methods approach, including case study analysis and statistical modeling, the research highlights key improvements in inventory accuracy, logistics optimization, and fraud detection success rates. Findings indicate that retailers leveraging real-time analytics have experienced a 25-40% increase in supply chain efficiency, with major gains in demand forecasting precision and supplier risk assessments. Despite these advantages, challenges remain, including high implementation costs, data security vulnerabilities, and algorithmic biases. Smaller retailers face significant barriers in adopting AI-powered analytics due to infrastructure limitations and workforce constraints. The study emphasizes the importance of ethical AI governance, cybersecurity protocols, and regulatory compliance in ensuring responsible analytics adoption. Future research should focus on scalable AI frameworks, blockchain-enhanced supply chain security, and quantum computing applications in predictive analytics. The findings underscore the need for a multi-stakeholder approach that integrates technological innovation with ethical considerations to achieve sustainable, transparent, and resilient supply chain ecosystems. This research contributes to the ongoing discourse on data-driven retail transformation, offering strategic insights for industry leaders, policymakers, and researchers in supply chain management.