Abstract : The integration of Artificial Intelligence (AI) in retail has revolutionized customer engagement, inventory management, and operational strategies. Retailers globally are facing challenges such as supply chain disruptions, fluctuating consumer demands, and competitive pressures, necessitating innovative technological solutions. The review synthesizes emerging trends and strategies in AI-driven predictive analytics, focusing on inventory management, customer engagement, and supply chain optimization. The objective of this study is to critically evaluate recent developments in AI applications within retail, emphasizing predictive analytics, consumer behavior insights, and operational transformation. The novelty lies in combining insights from diverse AI-based techniques, including machine learning, big data, and predictive replenishment, to examine their role in addressing operational bottlenecks and enhancing consumer retention. Utilizing systematic literature review methodologies, recent peer-reviewed journal articles published from 2024 were analyzed to identify key trends and patterns. The methodology employed a systematic and transparent review framework, including eligibility criteria, study selection, data extraction, and synthesis. Articles emphasizing AI in retail were rigorously screened for methodological rigor and relevance. Using comprehensive searches, thematic analysis, and quality assessments, the review synthesized trends, innovations, and challenges, offering actionable insights and future research directions. Results highlight that AI-driven tools such as predictive replenishment models, consumer behavior analysis, and demand forecasting significantly optimize supply chain processes, improve stock availability, and enhance customer satisfaction. Furthermore, the study identifies the role of AI in promoting personalized e-marketing strategies, improving demand forecasting accuracy, and leveraging real-time data for predictive insights. The findings underscore AI’s transformative potential in addressing challenges like customer engagement and operational inefficiencies, aligning with emerging retail 4.0 paradigms. The implications suggest that retailers adopting AI technologies achieve competitive advantages by leveraging data-driven insights. However, limitations include technological adaptation costs and ethical concerns related to data privacy. Future research should focus on exploring cross-industry AI integration, regulatory frameworks, and long-term sustainability in AI implementation. The review offers practical insights for policymakers and retailers aiming to integrate AI technologies into their operations while maintaining ethical digital responsibility.